Search

This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer's behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals' acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers' choices, but also those that studied consumers' decisions as a result of the interactions that take place among the received marketing messages and the individual's internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer's behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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).

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.

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, F z achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Electroencephalography (EEG) offers insights into the neural responses of consumers to marketing stimuli which can be used to evaluate the effectiveness of such stimuli in directing favorable consumer behavior in real-world settings. This practical review provides guidelines for using EEG in consumer and marketing research. It provides recommendations for how EEG may effectively be employed in neuromarketing in the future. EEG requires careful experimental design, and as such, we outline current recommendations for the collection, processing, analysis, and interpretation of EEG evoked potentials (i.e., event related potentials; ERP) and spectral content (i.e., EEG frequency). By providing an introductory overview of EEG measures in marketing and consumer research, this practical review extends previous literature that is primarily focused on other neuroimaging techniques (e.g., functional magnetic resonance imaging) and other disciplines (e.g., economics and organizational behaviour). Furthermore, by reviewing how EEG has been used throughout psychophysiological and neuromarketing research, we provide recommendations on how EEG can be used to measure marketing-related outcomes. These include processes relating to perception, attention, memory, emotion, and cognitive load, demonstrating the unique value of considering the neural responses captured by EEG in understanding and predicting consumer behavior. [ABSTRACT FROM AUTHOR]

Neuromarketing uses brain‐computer interface technology to understand customer preferences in response to marketing stimuli. Every year, marketing professionals spend over $750 Billion (US dollars) on traditional marketing, which is usually behavioral and subjective, focusing on self‐reports acquired via questionnaires, focus groups, and depth interviews. Neuromarketing, on the other hand, promises to overcome such limitations. This work proposes a machine learning framework that incorporates multiple components (endorsement, offer, and slogan) in real advertisement to predict consumer preference from electroencephalography (EEG) signals. In addition, we also use eye‐tracking data to visualize consumer viewing patterns according to both advertisement type and preference. EEG signals are collected from 22 healthy volunteers while viewing the real ads as stimuli. After preprocessing the signals, three‐domain features are extracted (time, frequency, and time‐frequency). Then, using wrapper‐based approaches we choose best features which are later classified into strong and weak preferences using the support vector machine. The experimental results demonstrate the best performance using all the frontal channels with an accuracy of 96.97%, sensitivity of 96.30%, and specificity of 97.44%. Additionally, eye tracking data reveals that subjects substantially prefer an ad, when they first glance at the endorsement. In addition, people tend to blink their eyes less frequently while viewing ads with endorsements and strongly prefer these commercials too. Additionally, our work lays the door for deploying such a neuromarketing framework in a real‐world context by employing consumer‐grade EEG equipment. Therefore, it is evident that neuromarketing technology may assist brands and companies in accurately predicting future customer preferences. [ABSTRACT FROM AUTHOR]

Competition in the airline transportation sector has significantly intensified following the deregulation act in the United States. To remain competitive, airline companies have begun implementing a range of strategies. One of the strategies commonly used by airlines is to improve service quality. Service quality has been very beneficial for airline companies to maintain profitability and increase market share. To achieve this goal, airlines must investigate the factors influencing service quality and enact improvements and regulations based on these determinants. There are significant studies in the literature on the influence of service employees on service quality. However, there are only a limited number of studies on the influence of the service quality of flight attendants, who are the public face of airline operations, on service quality. This study focuses on the influence of the level of attractiveness (a nonverbal characteristic) of flight attendants on perceived service quality. This research employs a neuromarketing method, specifically the frontal asymmetry approach, using electroencephalography (EEG) techniques. In the experiment, 37 participants were recruited, including 19 males and 18 females, and frontal alpha asymmetry metric results were derived from electrode pairs. In the experiment conducted at the Faculty of Aeronautics and Astronautics, Eskisehir Technical University, participants also evaluated the influence of flight attendant attractiveness on service quality using a survey technique. The findings indicate a significant difference in frontal alpha asymmetry (FAA) metrics between attractive and unattractive flight attendants. FFA metrics of attractive flight attendants were greater than those of unattractive flight attendants. Additionally, FAA metrics were found to be greater under favourable service delivery than under unfavourable service delivery. Furthermore, among the service dimensions, the reliability and responsiveness dimensions had higher FAA metrics than did the other dimensions. A study comparing EEG results with survey responses revealed a statistically significant difference. The findings indicate that flight attendants with higher levels of attractiveness enhance their motivation to approach, thereby contributing to an improvement in perceived service quality. Moreover, these findings underscore the potential of neuromarketing methods for validating outcomes from conventional research approaches. Based on these findings, airlines should develop policies that prioritize attractive attributes in the recruitment of flight attendants. In addition, implementing various programs and incentive systems to maintain the attractiveness of flight attendants is essential. Moreover, providing services with diversified flight attendant profiles tailored to different customer segments is considered crucial for customer satisfaction and experience. Future studies could achieve more comprehensive results by using diverse sample sizes and age groups along with different neuromarketing techniques. Furthermore, the findings suggest a high potential for applying the frontal alpha asymmetry approach in other marketing fields. [ABSTRACT FROM AUTHOR]

The investigation of olfactory stimuli has become more prominent in the context of neuromarketing research over the last couple of years. Although a few studies suggest that olfactory stimuli are linked with consumer behavior and can be observed in various ways, such as via electroencephalogram (EEG), a universal method for the detection of olfactory stimuli has not been established yet. In this study, 14-channel EEG signals acquired from participants while they were presented with 2 identical boxes, scented and unscented, were processed to extract several linear and nonlinear features. Two approaches are presented for the classification of scented and unscented cases: i) using machine learning (ML) methods utilizing extracted features; ii) using deep learning (DL) methods utilizing relative sub-band power topographic heat map images. Experimental results suggest that the olfactory stimulus can be successfully detected with up to 92% accuracy by the proposed method. Furthermore, it is shown that topographic heat maps can accurately depict the response of the brain to olfactory stimuli. [ABSTRACT FROM AUTHOR]

This article discusses a study that used electroencephalogram (EEG) to examine the effects of television commercials on viewers. The study found that likability, attractiveness, and emotions are related to the prefrontal cortex, and that theta activation in the frontal region is associated with increased empathy and purchasing rates. The study also describes the methodology used, including the use of questionnaires and EEG recordings. The results of the questionnaires showed that advertisements can change attitudes towards products and create a desire to purchase. [Extracted from the article]

This article discusses the use of electroencephalography (EEG) in neuromarketing research. It provides a list of articles that have applied EEG to study various aspects of marketing, such as product preferences, advertising effectiveness, brand extension, and consumer behavior. The article also presents a proposed model for the experimental process in neuromarketing research using EEG. The model was validated by experts in the field. Overall, the article highlights the growing interest in using EEG as a tool to understand consumer responses and decision-making processes in marketing. [Extracted from the article]

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]

The research objective is to test the effects of heritage on the Tourist Destination Image formation. A multi-method quantitative approach applying surveys and neuromarketing were applied. Three different surveys were carried out in three different moments of the touristic visit to measure the heritage impact on the dynamics of destination image. Furthermore, a neuromarketing experiment using electroencephalogram and virtual reality were carried out to measure the emotional and cognitive responses to heritage and their effects on the TDI formation. Results show, either the surveys or the neuromarketing experiment, a clear emotional response to heritage that, indeed, has a positive effect in the cultural destination image. The conclusion suggests a line of research for the development and improvement of TDI models for cultural destinations which would incorporate the in situ emotional response in an appraisal approach to emotions. [ABSTRACT FROM AUTHOR]

The use of neuroimaging to predict individual and population-wide behaviors, also known as neuroforecasting, was long applied to estimate movie popularity. Only recently, EEG-based neural synchronization, which is indicative of engagement, was found as a valid predictor of the listening behavior of the population. However, the population's evaluative responses to the songs were not incorporated. To fill this void, this study explored whether neural synchrony can also be related to likes, dislikes and comments for the same songs on YouTube more than two years after their release. In this way, we aimed to separate passive engagement (i.e., listening) from active engagement (evaluating). The results showed that neural synchrony was a significant predictor of the likes and comments on YouTube, even after controlling for explicit liking ratings from the lab study. In contrast, frontal alpha asymmetry did not predict YouTube likes. Thus, engagement as represented by neural synchronization could be a valuable tool for predicting active as well as passive engagement with entertainment products. This underlines the value of neural similarity in predicting the impact of music and videos before their true effect in the crowd can be known. [ABSTRACT FROM AUTHOR]

There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects’ willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product’s image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike. [ABSTRACT FROM AUTHOR]

In an era of increased competition between companies, the retention, as well as the acquisition of new clients constitutes one of the greatest tasks for the management of any company. In order to successfully reach target clients and influence their purchasing decisions, marketing specialists utilise various forms of communication, including mass communication, of which the primary tool is advertising. The aim of this article is to present the results of a study on the effectiveness of advertising communication conducted through the use of electroencephalography (EEG). The author argues that a company utilising this method may significantly improve its existing knowledge of clients and facilitate the creation of appropriate strategies for the future, e.g. product launch strategies. [ABSTRACT FROM AUTHOR]

Copyright of Index.Comunicación is the property of Index.comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Neuromarketing techniques have expanded significantly in recent years. Therefore, in the present research, the role of endorsement strategy on the consumers' attention to sports products is investigated by examining alpha and beta brain waves. The research method was done experimentally. The statistical population consisted of 20 males and females in the age range of 25 to 35 years. Images according to the endorsement strategy used with Enobio, a 20-channel electroencephalography device to record waves and measure attention. Frontal (F3, F4, FZ), central (C3, C4, CZ), temporal (T7, T8), occipital (O1, O2) and parietal (PZ) brain regions examined. In order to extract waves from quantitative electroencephalography data, Matlab 2013 b software was used. Mann-Whitney U test used to analyze the data using SPSS 22 software. The results showed that there is a significant difference between alpha and beta waves between two groups, without endorsement and with endorsement images, so the endorsement strategy reduces the amount of alpha waves and increases the amount of beta waves in different areas of the brain. Endorsement can attract the attention of the consumer significantly. It can be employed as an effective method to attract the attention of consumers and promote them to purchase sports product. [ABSTRACT FROM AUTHOR]

Abstract Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers’ choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (“buy”/ “not buy”), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field.

Electroencephalography (EEG)-based emotion recognition is an important technology for human–computer interactions. In the field of neuromarketing, emotion recognition based on group EEG can be used to analyze the emotional states of multiple users. Previous emotion recognition experiments have been based on individual EEGs; therefore, it is difficult to use them for estimating the emotional states of multiple users. The purpose of this study is to find a data processing method that can improve the efficiency of emotion recognition. In this study, the DEAP dataset was used, which comprises EEG signals of 32 participants that were recorded as they watched 40 videos with different emotional themes. This study compared emotion recognition accuracy based on individual and group EEGs using the proposed convolutional neural network model. Based on this study, we can see that the differences of phase locking value (PLV) exist in different EEG frequency bands when subjects are in different emotional states. The results showed that an emotion recognition accuracy of up to 85% can be obtained for group EEG data by using the proposed model. It means that using group EEG data can effectively improve the efficiency of emotion recognition. Moreover, the significant emotion recognition accuracy for multiple users achieved in this study can contribute to research on handling group human emotional states. [ABSTRACT FROM AUTHOR]

Neuromarketing or consumer neuroscience is a relatively new market research subdiscipline that has gained popularity among consumer behavior scientists in the past two decades or so. It combines neurobiology with behavioral psychology to understand consumer behavior, more specifically about their decisions related to choices/preferences and purchase. The purpose of this review is to explore the potential of using neuroscientific methods for consumer sensory science research. By no means, this is an exhaustive review hindered by the fact that there are countless articles on neuromarketing and consumer neuroscience in the literature. The author has tried to show the applicability of neuroscientific methods in consumer sensory sciences, specifically electroencephalography and eye tracking, which could potentially "complement" the sensory methodologies to gain better consumer insight. Both these techniques are relatively inexpensive, portable, and minimally invasive techniques that are already being used by some sensory scientists. They could be incorporated with ease in the research portfolio of consumer sensory researchers who would like to use them to study consumer affect. It is recommended that the researchers use proper experimental design that takes into consideration the confounding variables as much as possible. The two methods mentioned before have been proven to be relatively reliable and repeatable. Lastly, these methods would also require ethical oversight because of the involvement of human subjects. [ABSTRACT FROM AUTHOR]

Neuromarketing is a modern marketing research technique whereby consumers' behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers' responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers' preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing. [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]

Although neuromarketing research has developed, the current studies lack to provide comprehensive insights into neuromarketing and marketing mix. Therefore, this study has designed to provide a comprehensive overview of neuromarketing, classification of neuroimaging and physiological tools are currently used in the marketing mix, and highlights the neural responses of consumer's behavior (e.g., emotions, attention, motivation, reward processing, and perception) to be considered in the marketing mix. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to select relevant documents for this article. In this study, 106 articles and review papers were extracted and analyzed from the Web of Science database to fill the gap in the literature. We found that 10 tools have been used in studying the marketing mix, such as advertising, brand, price, and product). For example, electroencephalography was the most applied tool, while advertising was the most marketing mix employed. We also found that the frontal and temporal gyri were correlated with pleasure/displeasure and high/low arousal. The occipital lobe is linked to attention processes, while the hippocampus relates to long and short-term memory. Such findings provide valuable insights into the neural responses in marketing mix research. [ABSTRACT FROM AUTHOR]

Mobile payment has emerged as a popular payment method in many countries. While much research has focused on the antecedents of mobile payment adoption, limited research has investigated the consequences of mobile payment usage relating to how it would influence consumer behaviors (e.g., purchase intention or willingness to pay). Here, we propose that mobile payment not just reduces the "pain of paying," a traditional view explaining why cashless payment stimulates spending, but it also evokes the "pleasure of paying," raising from the enhanced processing fluency in completing transactions. We tested this new conceptualization of "pleasure of paying" using EEG, complementing other behavioral measures. In two studies, we found that mobile payment effectively enhanced purchase likelihood (study 1, N = 66) and such an enhancement is generalizable to both hedonic and utilitarian products (study 2, N = 29). By employing EEG measures, we provided the first neural evidence of "pleasure of paying" in addition to the signal of "pain of paying." Critically, we demonstrated that the "pleasure of paying" is a distinctive psychological mechanism that is induced by mobile payment usage and that the "pleasure of paying" joins the "pain of paying" to mediate the increased purchase intention. We discuss the contributions and implications of these results to the ongoing evolution of cashless payment societies. [ABSTRACT FROM AUTHOR]

While neuromarketing research delineates how specific neurophysiological methods offer predictive power beyond traditional survey methods, few studies explore how these methods can be used concurrently to measure outcomes across marketing contexts. We address this gap by simultaneously eliciting multiple neurophysiological measures: inter‐beat interval from heart rate, skin conductance response, and frontal alpha asymmetry from electroencephalography (EEG) across multiple stimulus categories, and associating them with distinct psychological (emotional valence and arousal) and marketing (ad recognition and ad effectiveness) outcomes. We found that inter‐beat interval was associated with emotional valence while skin conductance response was associated with emotional arousal across all stimulus types. Critically, self‐reported arousal and inter‐beat interval at both the aggregate and frame level were associated with ad recognition, but self‐reported valence was not. Finally, inter‐beat interval and frontal alpha asymmetry played complementary roles in predicting out‐of‐sample ad effectiveness. Specifically, while inter‐beat interval was a significant predictor of out‐of‐sample brand recognition and not purchase intention, frontal alpha asymmetry was associated with purchase intention and not brand recognition components of ad effectiveness. Our results demonstrate distinct and complementary marketing applications for inter‐beat interval, skin conductance response, and frontal alpha asymmetry and provide valuable insights to practitioners seeking to incorporate findings from these neurophysiological methodologies into their marketing decisions. [ABSTRACT FROM AUTHOR]

This research aims to examine a neural network (artificial intelligence) as an alternative model to examine the neuromarketing phenomenon. Neuromarketing is comparatively new as a technique for designing marketing strategies, especially advertising campaigns. Marketers have used a variety of different neuromarketing tools, for instance functional magnetic resonance imaging (fMRI), eye tracking, electroencephalography (EEG), steady-state probe topography (SSPT), and other expensive gadgets. Similarly, researchers have been using these devices to carry out their studies. Therefore, neuromarketing has been an expensive project for both companies and researchers. We employed 585 human responses and used the neural network (artificial intelligence) technique to examine the predictive consumer buying behavior of an effective advertisement. For this purpose, we employed two neural network applications (artificial intelligence) to examine consumer buying behavior, first taken from a 1–5 Likert scale. A second application was run to examine the predicted consumer buying behavior in light of the neuromarketing phenomenon. The findings suggest that a neural network (artificial intelligence) is a unique, cost-effective, and powerful alternative to traditional neuromarketing tools. This study has significant theoretical and practical implications for future researchers and brand managers in the service and manufacturing sectors. [ABSTRACT FROM AUTHOR]

This research focuses on the communication of typical–local foods and their consumption as a main motivation for travel experiences and driver of food tourism attractiveness. Past studies have provided an inconsistent understanding of this phenomenon and its underlying psychological processes. Others have called for research on more integrated and effective marketing communications about typical–local foods to increase destination attractiveness, help consumer decision‐making processes and reduce information asymmetry in the food tourism market. Through the application of an integrated framework under the umbrella of signaling theory, we focus on various signals (e.g., "food security," "trusting relationships," "emotional value") associated with three key features of typical–local foods: sustainability, geographical indications and healthiness. We use a combination of neuroscience techniques, including the implicit priming test, eye‐tracking and electroencephalography, to investigate which signals are best at communicating typical–local foods' distinctiveness. We find that consumers/tourists perceive healthiness as typical–local foods' most engaging and attractive product feature, followed by geographical indications then sustainability. Specific signals impact people's visual attention and cognitive engagement differently, with social and emotional attributes being most appealing. Therefore, food tourism managers should focus on these attributes in their communication campaigns to increase food tourism attractiveness and enhance travel experience. [ABSTRACT FROM AUTHOR]

Copyright of Olsztyn Economic Journal is the property of Olsztyn Economic Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $ 750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences. [ABSTRACT FROM AUTHOR]

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

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).

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. Image-based saliency estimation being participant independent, the estimation from EEG could take into account the subject specificity. For this purpose, a set of signals has been recorded, and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. Being able to predict a visual saliency map from EEG could help in research studying the relationship between brain activity and visual attention. It could also help in various applications: vigilance assessment during driving, neuromarketing, and also in the help for the diagnosis and treatment of visual attention-related diseases. For the sake of reproducibility, the codes and dataset considered in this paper have been made publicly available to promote research in the field. [ABSTRACT FROM AUTHOR]

Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers’ choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (“buy”/ “not buy”), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field. [ABSTRACT FROM AUTHOR]

Neuromarketing is an emerging research field for prospective businesses on consumer's preference. Consumer's preference prediction based on electroencephalography (EEG) can reliably predict likes or dislikes of a product. However, the current EEG prediction and classification accuracy have yet to reach ideal level. In addition, it is still unclear how different brain region information and different features such as power spectral density, brain asymmetry, differential entropy, and Hjorth parameters affect the prediction accuracy. Our study shows that by taking footwear products as an example, the recognition accuracy of product likes or dislikes reaches 94.22%. Compared with other brain regions, the features of the frontal and occipital brain region obtained a higher prediction accuracy, but the fusion of the features of the whole brain region could improve the prediction accuracy of likes or dislikes even further. Future work would be done to correlate the EEG-based like or dislike prediction results with product sales and self-reports. [ABSTRACT FROM AUTHOR]

This study investigates the prospects of using electroencephalography (EEG) in tourism and hospitality research. It first discusses the concept and importance of EEG. Then, it systematically reviews articles that have used EEG to measure psychophysiological responses in business and management. The review mainly discusses EEG themes/theories/concepts and EEG methods and indices. Based on the review, this study analyses main challenges of applying EEG and suggests solutions to solve them. It finally proposes some future applications of EEG in tourism and hospitality. This study is one of the first to offer a better understanding of the literature, analysis methods, and theories/concepts associated with EEG, informing future EEG applications in tourism and hospitality research. [ABSTRACT FROM AUTHOR]

This article aims to clarify the use and potential of electroencephalography methods in neuromarketing research. Identify the main advantages and benefits of the method as well as the prerequisites for its use. The results of research using the EEG method and their use are a secondary goal of the scientific article. We focus primarily on research and technology with the use of hardware and software. The article focuses primarily on the EEG method and its main advantages and functions. Subsequent examination of the human brain during neuromarketing research focused on consumer behavior. This article is intended to point out relevant facts and findings that can be obtained by the EEG method during neuromarketing research. The output points to the possibilities and application of the EEG in the future. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses. [ABSTRACT FROM AUTHOR]

Neuromarketing is an emerging research field for prospective businesses on consumer’s preference. Consumer’s preference prediction based on electroencephalography (EEG) can reliably predict likes or dislikes of a product. However, the current EEG prediction and classification accuracy have yet to reach ideal level. In addition, it is still unclear how different brain region information and different features such as power spectral density, brain asymmetry, differential entropy, and Hjorth parameters affect the prediction accuracy. Our study shows that by taking footwear products as an example, the recognition accuracy of product likes or dislikes reaches 94.22%. Compared with other brain regions, the features of the frontal and occipital brain region obtained a higher prediction accuracy, but the fusion of the features of the whole brain region could improve the prediction accuracy of likes or dislikes even further. Future work would be done to correlate the EEG-based like or dislike prediction results with product sales and self-reports.

• Except for both sides of the temporal lobe, all the brain subdivisions considered in this study showed a significant difference between like and dislike groups at 4000 ms. • The EEG activity showed a significant increase in the right parietal and the left occipital lobes at 500 ms, the right hemisphere of the brain and both sides of the occipital lobe at 750 ms and the right occipital lobe at 2000 ms for the like group. • No significant changes were reported in the dislike group. • The classification step at 4000 ms showed that the Neural Network classifier achieved the best performance. Neuromarketing involves the study of brain responses that focuses on understanding how consumers' brains respond to products and services, and how these responses influence their choice. Evidence suggests that electroencephalography (EEG) can provide valuable insight into consumer preferences and decision-making processes. This study aims is to assess the relative importance of right/left brain regions (including hemispheres, frontal, temporal, parietal, and occipital lobes) in the consumer choice towards E-commerce products. Also, this study aims to distinguishes the EEG characteristics of consumers' preference using a classification system. Using a publicly available EEG neuromarketing dataset, the change in EEG signals has been evaluated by a mixed model for repeated measures for all brain regions. Four classification algorithms (k-Nearest Neighbor, Random Forest, Neural Network, and Gradient Boosting) were used to distinguish like and dislike preferences. Greater EEG activity in the right hemisphere, right parietal, right occipital, and left occipital was related to like responses. Except for both sides of the temporal lobe, all the subdivisions of the brain considered showed a significant decrease of activity at 4000 ms for like-related responses. However, no significant change in the activity was related to the dislike response. The highest AUC of the four classifiers used was as follows: 76.61% for the right parietal lobe with Neural Network, 75.33% for the left parietal lobe with Gradient Boosting, 73.55% for the right frontal lobe with k-Nearest Neighbor and 72.62% for the right frontal lobe with Random Forest. Considering the significant difference between like and dislike responses at 4000 ms, Neural Network showed the best performance followed by Gradient Boosting. Our framework suggests that the formation of preferences (like and dislike) requires different patterns of brain activity and that Neural Network and Gradient Boosting are valuable tools for distinguishing consumer preference. [ABSTRACT FROM AUTHOR]

As more destination advertisements are uploaded on short video-sharing platforms, investigating the effects of in-stream advertising on destination advertising is important. This study examines the spillover effects of residual arousal produced by a prior advertisement on subsequent destination video advertising. It develops and tests new hypotheses by connecting outside appearance (emotion and behavioural intention) and inside neural mechanism (brain activity) using electroencephalography. This study contributes to the theoretical advancement of increasing the explanation power of excitation transfer theory applied to destination advertising. Results confirm the spillover effect. Residual excitation was found to energise female (rather than male) arousal significantly. The participants exposed to the prior advertisement are more likely to visit the advertised travel destinations than those who were not. • Spillover effects of a prior commercial on tourism advertisements were discovered. • Event-related potentials measured arousal induced by brain activities. • Residual excitation energised female arousal significantly more than male arousal. • Subjects who viewed a prior commercial were more likely to visit the destination. • Excitation transfer was tested and confirmed in a tourism advertising context. [ABSTRACT FROM AUTHOR]