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Panteli A, Kalaitzi E, Fidas CA. A review on the use of eeg for the investigation of the factors that affect Consumer's behavior. Physiol Behav 2024; 278:114509. [PMID: 38485039 DOI: 10.1016/j.physbeh.2024.114509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/20/2024]
Abstract
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.
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Affiliation(s)
- Antiopi Panteli
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece.
| | - Eirini Kalaitzi
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
| | - Christos A Fidas
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
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2
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Deng Y, Wang Y, Xu L, Meng X, Wang L. Do you like it or not? Identifying preference using an electroencephalogram during the viewing of short videos. Psych J 2023. [PMID: 37186458 DOI: 10.1002/pchj.645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 02/08/2023] [Indexed: 05/17/2023]
Abstract
Accurately predicting whether a short video will be liked by viewers is a topic of interest to media researchers. This study used an electroencephalogram (EEG) to record neural activity in 109 participants as they watched short videos (16 clips per person) to see which neural signals reflected viewers' preferences. The results showed that, compared with the short videos they disliked, individuals would experience positive emotions [indexed by a higher theta power, lower (beta - theta)/(beta + theta) score], more relaxed states (indexed by a lower beta power), lower levels of mental engagement and alertness [indexed by a lower beta/(alpha + theta) score], and devote more attention (indexed by lower alpha/theta) when watching short videos they liked. We further used artificial neural networks to classify the neural signals of different preferences induced by short videos. The classification accuracy was the highest when using data from bands over the whole brain, which was 75.78%. These results may indicate the potential of EEG measurement to evaluate the subjective preferences of individuals for short videos.
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Affiliation(s)
- Yaling Deng
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
- Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, China
| | - Ye Wang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
- Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, China
| | - Liming Xu
- School of Journalism, Communication University of China, Beijing, China
| | - Xiangli Meng
- School of International Studies, Communication University of China, Beijing, China
| | - Lingxiao Wang
- School of Animation and Digital Art, Communication University of China, Beijing, China
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3
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Byrne A, Bonfiglio E, Rigby C, Edelstyn N. A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research. Brain Inform 2022; 9:27. [PMCID: PMC9663791 DOI: 10.1186/s40708-022-00175-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Abstract
Introduction
The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.
Methods
Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.
Results
Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.
Conclusions and implications
FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.
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Kaklauskas A, Abraham A, Ubarte I, Kliukas R, Luksaite V, Binkyte-Veliene A, Vetloviene I, Kaklauskiene L. A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States. SENSORS (BASEL, SWITZERLAND) 2022; 22:7824. [PMID: 36298176 PMCID: PMC9611164 DOI: 10.3390/s22207824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/28/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.
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Affiliation(s)
- Arturas Kaklauskas
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ajith Abraham
- Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA 98071, USA
| | - Ieva Ubarte
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Romualdas Kliukas
- Department of Applied Mechanics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Vaida Luksaite
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Arune Binkyte-Veliene
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ingrida Vetloviene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Loreta Kaklauskiene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
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5
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Khurana V, Gahalawat M, Kumar P, Roy PP, Dogra DP, Scheme E, Soleymani M. A Survey on Neuromarketing Using EEG Signals. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3065200] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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6
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Neuroimaging Techniques in Advertising Research: Main Applications, Development, and Brain Regions and Processes. SUSTAINABILITY 2021. [DOI: 10.3390/su13116488] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Despite the advancement in neuroimaging tools, studies about using neuroimaging tools to study the impact of advertising on brain regions and processes are scant and remain unclear in academic literature. In this article, we have followed a literature review methodology and a bibliometric analysis to select empirical and review papers that employed neuroimaging tools in advertising campaigns and to understand the global research trends in the neuromarketing domain. We extracted and analyzed sixty-three articles from the Web of Science database to answer our study questions. We found four common neuroimaging techniques employed in advertising research. We also found that the orbitofrontal cortex (OFC), the ventromedial prefrontal cortex, and the dorsolateral prefrontal cortex play a vital role in decision-making processes. The OFC is linked to positive valence, and the lateral OFC and left dorsal anterior insula related in negative valence. In addition, the thalamus and primary visual area associated with the bottom-up attention system, whereas the top-down attention system connected to the dorsolateral prefrontal cortex, parietal cortex, and primary visual areas. For memory, the hippocampus is responsible for generating and processing memories. We hope that this study provides valuable insights about the main brain regions and processes of interest for advertising.
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Alvino L, Pavone L, Abhishta A, Robben H. Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research. Front Neurosci 2020; 14:577666. [PMID: 33343279 PMCID: PMC7744482 DOI: 10.3389/fnins.2020.577666] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 11/03/2020] [Indexed: 12/28/2022] Open
Abstract
The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.
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Affiliation(s)
- Letizia Alvino
- Center for Marketing and Supply Chain Management, Nyenrode Business University, Breuklen, Netherlands
| | - Luigi Pavone
- Neuromed, Mediterranean Neurological Institute, Isernia, Italy
| | - Abhishta Abhishta
- Hightech Business and Entrepreneurship Group (HBE), University of Twente, Enschede, Netherlands
| | - Henry Robben
- Center for Marketing and Supply Chain Management, Nyenrode Business University, Breuklen, Netherlands
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8
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Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inform 2020; 7:10. [PMID: 32955675 PMCID: PMC7505913 DOI: 10.1186/s40708-020-00109-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 08/14/2020] [Indexed: 11/20/2022] Open
Abstract
Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
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An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy. SOCIAL SCIENCES-BASEL 2020. [DOI: 10.3390/socsci9090162] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.
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10
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Sargent A, Watson J, Ye H, Suri R, Ayaz H. Neuroergonomic Assessment of Hot Beverage Preparation and Consumption: An EEG and EDA Study. Front Hum Neurosci 2020; 14:175. [PMID: 32499688 PMCID: PMC7242644 DOI: 10.3389/fnhum.2020.00175] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 04/20/2020] [Indexed: 12/22/2022] Open
Abstract
Neuroergonomics is an emerging field that investigates the human brain about behavioral performance in natural environments and everyday settings. This study investigated the body and brain activity correlates of a typical daily activity, hot beverage preparation, and consumption in a realistic office environment where participants performed natural daily tasks. Using wearable, battery operated and wireless Electroencephalogram (EEG) and Electrodermal activity (EDA) sensors, neural and physiological responses were measured in untethered, freely moving participants who prepared hot beverages using two different machines (a market leader and follower as determined by annual US sales). They later consumed the drinks they had prepared in three blocks. Emotional valence was estimated using frontal asymmetry in EEG alpha band power and emotional arousal was estimated from EDA tonic and phasic activity. Results from 26 participants showed that the market-leading coffee machine was more efficient to use based on self-reports, behavioral performance measures, and there were significant within-subject differences in valence between the two machine use. Moreover, the market leader user interface led to greater self-reported product preference, which was further supported by significant differences in measured arousal and valence (EDA and EEG, respectively) during coffee production and consumption. This is the first study that uses a multimodal and comprehensive assessment of coffee machine use and beverage consumption in a naturalistic work environment. Approaches described in this study can be adapted in the future to other task-specific machine usability and consumer neuroscience studies.
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Affiliation(s)
- Amanda Sargent
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Jan Watson
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Hongjun Ye
- Lebow College of Business, Drexel University, Philadelphia, PA, United States
| | - Rajneesh Suri
- Lebow College of Business, Drexel University, Philadelphia, PA, United States.,Drexel Solutions Institute, Drexel University, Philadelphia, PA, United States
| | - Hasan Ayaz
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States.,Drexel Solutions Institute, Drexel University, Philadelphia, PA, United States.,Department of Psychology, College of Arts and Sciences, Drexel University, Philadelphia, PA, United States.,Department of Family and Community Health, University of Pennsylvania, Philadelphia, PA, United States.,Center for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, PA, United States
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11
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Cartocci G, Modica E, Rossi D, Cherubino P, Maglione AG, Colosimo A, Trettel A, Mancini M, Babiloni F. Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population. Front Hum Neurosci 2018; 12:231. [PMID: 30210322 PMCID: PMC6124418 DOI: 10.3389/fnhum.2018.00231] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 07/25/2018] [Indexed: 01/04/2023] Open
Abstract
Tobacco constitutes a global emergency with totally preventable millions of deaths per year and smoking-related illnesses. Public service announcements (PSAs) are the main tool against smoking and by now their efficacy is still assessed through questionnaires and metrics, only months after their circulation. The present study focused on the young population, because at higher risk of developing tobacco addiction, investigating the reaction to the vision of Effective, Ineffective and Awarded antismoking PSAs through: electroencephalography (EEG), autonomic activity variation (Galvanic skin response—GSR- and Heart Rate—HR-) and Eye-Tracking (ET). The employed indices were: the EEG frontal alpha band asymmetry and the frontal theta; the Emotional Index (EI), deriving from the GSR and HR signals matching; the ET Visual Attention (VA) index, based on the ratio between the total time spent fixating an area of interest (AOI) and its area. Smokers expressed higher frontal alpha asymmetry values in comparison to non-smokers. Concerning frontal theta, Awarded PSAs reported the highest values in comparison to both Effective and Ineffective PSAs. EI results highlighted that lowest values were expressed by Heavy Smokers (HS), and Effective PSAs obtained the highest EI values. Finally, concerning the Effective PSAs, regression analysis highlighted a correlation between the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values. ET results suggested that for the Ineffective PSAs the main focus were texts, while for the Effective and Awarded PSAs were the visual elements. Results support the use of methods aimed at assessing the physiological reaction for the evaluation of PSAs images, in particular when considering the smoking habits of target populations.
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Affiliation(s)
- Giulia Cartocci
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Enrica Modica
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Dario Rossi
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Rome, Italy
| | | | | | - Alfredo Colosimo
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Rome, Italy
| | | | | | - Fabio Babiloni
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,Department of Computer Science, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, China
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12
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Ortuño FM, Valenzuela O, Glösekötter P, Rojas I. Main findings and advances in biomedical engineering and bioinformatics from IWBBIO 2015. Biomed Eng Online 2016; 15 Suppl 1:79. [PMID: 27454414 PMCID: PMC4959334 DOI: 10.1186/s12938-016-0187-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Franscisco M. Ortuño
- Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, 18071 Granada, Spain
| | - Olga Valenzuela
- Department of Applied Mathematics, University of Granada, 18071 Granada, Spain
| | - Peter Glösekötter
- Department of Electrical Engineering and Computer Sciences, Muenster University of Applied Sciences, 48565 Steinfurt, Germany
| | - Ignacio Rojas
- Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, 18071 Granada, Spain
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