The Study of Electroencephalography in Neuromarketing Research, Consumer Behavior and Performance Method: A Systematic Analysis

Journal of System Management

دوره 9، شماره 4 - شماره پیاپی 36، دی 2023، صفحه 185-204   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/jsm.2023.1983505.1805

نویسندگان

Samira Nazari Ghazvini¹؛ Younos Vakil Alroaia^{* 2}؛ Rohizat Bin Baharon³

¹PhD Student in Business Management, Semnan Branch, Islamic Azad University, Semnan, Iran

²Associate Prof. and Chairman, Entrepreneurship and Commercialization Research Center, Semnan Branch, Islamic Azad University, Semnan, Iran

³Professor in Marketing Management, Department of Business Administration, Universiti Teknologi Malaysia, Johor, Malaysia

چکیده

Neuromarketing and its tools are an emerging and evolving field that bridges the gap between consumer behavior studies and neuroscience. These new technologies are used in the field of neuromarketing and neuroscience to observe the brain areas involved, for example, in seeing, hearing, or smelling the product. For example, functional magnetic resonance imaging (FMRI), electroencephalography (EEG), and event-related potentials (ERP) are used to identify the active points in the consumer's brain when they see, smell, or hear the product or service being tested. How these different techniques and tools work in neuromarketing has always been somewhat confusing to marketers. This study aims to examine the research articles published on the ScienceDirect website by identifying the current state of neuromarketing tools and electroencephalography (EEG) in neuromarketing to identify research gaps and provide recommendations for the application of this technique for experimental research for marketers. Using a two-stage bibliographic methodology, we show that although "neuromarketing" is the focus of research on the ScienceDirect website, there are not many empirical studies examining electroencephalography (EEG) and the combined use of this technology in consumer behavior. However, the use of this tool to screen messages and promotional materials has received the most attention. This study highlights the limitations of a systematic investigation of EEG issues in neuromarketing by examining FMRI, EEG, and ERP technologies. It also focuses on consumer behavior, particularly online shopping decision making, website design, and consumers' physiological responses to the brand. In addition, future studies are expected to be completed by combining EEG and neuromarketing technologies.

کلیدواژه‌ها

"Neuromarketing"؛ " Electroencephalography"؛ " Bibliometric Analysis"؛ " Systematic literature review"

مراجع

Abesh Loui Aghdam, S., soltanpanah, H., Mahdieh, O., Shakeri, R., & Habibi, F. (2023). Presenting a Systemic Model for the Effectiveness of Marketing Communication Activities in Social Networks. Journal of System Management, 9(2), 153-167. https://doi.org/https://doi.org/10.30495/jsm.2023.1972982.1715

Ahlert, D., Kenning, P., and Plassmann, H. (2006). “A window to the consumer’s mind: application of functional brain imaging techniques to advertising research,” in International Advertising and Communication, eds S.

 https://doi.org/10.1007/3-8350-5702-2_9

Alvino, L. (2019). “How can we improve consumer behavior research? A critical literature review on the contributions and the limitations of consumer neuroscience,” in Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management Through Vision 2020 (Madrid), 5947–5951. https://b2n.ir/u50884

Alvino L, Pavone L, Abhishta A and Robben H (2020) Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research.Front. Neurosci. 14:577666.

 https://doi.org/10.3389/fnins.2020.577666

Avinash, T., Dikshant, L., & Seema, S. (2018). Methods of Neuromarketing and Implication of the Frontal Theta Asymmetry induced due to musical stimulus as choice modeling. Procedia Computer Science, 132, 55-67.

https://doi.org/https://doi.org/10.1016/j.procs.2018.05.059

Artêncio, M. M., Giraldi, J. d. M. E., & de Oliveira, J. H. C. (2022). A cup of black coffee with GI, please! Evidence of geographical indication influence on a coffee tasting experiment. Physiology & Behavior, 245, 113671.

https://doi.org/https://doi.org/10.1016/j.physbeh.2021.113671

Bastiaansen, M., Straatman, S., Driessen, E., Mitas, O., Stekelenburg, J., & Wang, L. (2018). My destination in your brain: A novel neuromarketing approach for evaluating the effectiveness of destination marketing. Journal of Destination Marketing & Management, 7, 76-88. https://doi.org/https://doi.org/10.1016/j.jdmm.2016.09.003

Banich, M. T., and Compton, R. J. (2011). Cognitive Neuroscience. 3rd ed.Wadsworth Cengage Learning. https://b2n.ir/t15994

Bell L, Vogt J, Willemse C, Routledge T, Butler LT and Sakaki M (2018) Beyond Self-Report: A Review of Physiological and Neuroscientific Methods to Investigate Consumer Behavior. Front. Psychol. 9:1655.

 https://doi.org/10.3389/fpsyg.2018.01655

Bhatia, Keshav. (2014). Neuromarketing: Towards a better understanding of consumer behavior. Optimization. Volume 6, Number 1. 52-62. ISSN no. 0974-0988

Bergstrom J. R., Schall A. J., (2017)  Eye Tracking in User Experience Design.

 https://b2n.ir/x15887

Bettiga, Debora, Lucio Lamberti, and Giuliano Noci (2017), “Do mind and body agree? Unconscious versus conscious arousal in product attitude formation, Journal of Business Research, 75, 108-117.

 https://doi.org/10.1016/j.jbusres.2017.02.008

Bercea, M. D. (2013). Quantitative versus qualitative in neuromarketing research Monica. Can. J. Region. Sci. 12, 39–60.

https://doi.org/10.1362/146934712X13286274424271

Berčík, J., Horská, E., Wang, R. W. Y., & Chen, Y.-C. (2016). The impact of parameters of store illumination on food shopper response. Appetite, 106, 101-109.

https://doi.org/https://doi.org/10.1016/j.appet.2016.04.010

Benda-Prokeinová, R., & Hanová, M. (2016). Consumer's Behavior of the Foodstuff Consumption in Slovakia. Procedia - Social and Behavioral Sciences, 220, 21-29.

https://doi.org/https://doi.org/10.1016/j.sbspro.2016.05.465

Bigne, E., Simonetti, A., Ruiz, C., & Kakaria, S. (2021). How online advertising competes with user-generated content in TripAdvisor. A neuroscientific approach. Journal of Business Research, 123, 279-288.

https://doi.org/https://doi.org/10.1016/j.jbusres.2020.10.010

Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4–5), 175–308.

 https://doi.org/10.1016/j.physrep.2005.10.009

Boz, H., Arslan, A., and Koc, E. (2017). Neuromarketing aspect of tourism pricing psychology. Tourism Manage. Perspect. 23, 119–128. https://doi.org/10.1016/j.tmp.2017.06.002

Boz, H., Arslan, A., & Koc, E. (2017). Neuromarketing aspect of tourısm pricing psychology. Tourism Management Perspectives, 23, 119-128.

https://doi.org/https://doi.org/10.1016/j.tmp.2017.06.002

BusinessDictionary.com. (2019). What is a consumer? definition and meaning. Retrieved from http://www.businessdictionary.com/definition/consumer.html Archived 2020-07-28 at the Wayback Machine.

Cabrera, F.E.; Sánchez-Núñez, P.; Vaccaro, G.; Peláez, J.I.; Escudero, J. Impact of Visual Design Elements andPrinciples in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks.Sensors 2021, 21, 4695.

 https://doi.org/10.3390/s21144695

Casado-Aranda, L.-A., Dimoka, A., & Sánchez-Fernández, J. (2019). Consumer Processing of Online Trust Signals: A Neuroimaging Study. Journal of Interactive Marketing, 47, 159-180. https://doi.org/https://doi.org/10.1016/j.intmar.2019.02.006

Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice.MIT Press. https://doi.org/10.7551/mitpress/9609.001.0001

Diehl and R. Terlutter (Germany: Deutscher Universitäts-Verlag), 163–178.

doi: 10.1007/3-8350-5702-2_9

Ebner, N. C., & Fischer, H. (2014). Emotion and aging: evidence from brain and behavior. Frontiers in Psychology, 5, 996.

https://doi.org/10.3389/fpsyg.2014.00996

Folwarczny, M., Pawar, S., Sigurdsson, V., & Fagerstrøm, A. (2019). Using neuro-IS/consumer neuroscience tools to study healthy food choices: a review. Procedia Computer Science, 164, 532-537.

https://doi.org/https://doi.org/10.1016/j.procs.2019.12.216

Georges, Patrick M (2014). Neuromarketing in Action: How to Talk and Sell to the Brain. London: Kogan Page Ltd. pp. 9–16. https://b2n.ir/g07698

Ghadimi, P., Wang, C., & Lim, M. (2019). Sustainable supply chain modeling and analytics; past debate, present problems, and future challenges. Resources, Conversation and Recycling, 140, 72–84.

https://doi.org/10.1016/j.resconrec.2018.09.005

Gill, R., & Singh, J. (2022). A study of neuromarketing techniques for proposing a cost-effective information-driven framework for decision making. Materials Today: Proceedings, 49, 2969-2981.

https://doi.org/https://doi.org/10.1016/j.matpr.2020.08.730

González-Morales, A., Mitrovic, J., & Garcia, R. C. (2020). Ecological consumer neuroscience for competitive advantage and business or organizational differentiation. European Research on Management and Business Economics, 26(3), 174-180.

https://doi.org/https://doi.org/10.1016/j.iedeen.2020.05.001

Giray, C., Yon, B., Alniacik, U., & Girisken, Y. (2022). How does mothers’ mood matter in their choice of organic food? Controlled eye-tracking study. Journal of Business Research, 144, 1175-1185.

https://doi.org/https://doi.org/10.1016/j.jbusres.2022.02.059

Global Harmonization Task Force (2012). Principles of Medical Devices Classification.

Gupta, R., ur Rehman Laghari, K., & Falk, T. H. (2016). Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing, 174, 875-884.

https://doi.org/https://doi.org/10.1016/j.neucom.2015.09.085

Goto, N., Mushtaq, F., Shee, D., Lim, X. L., Mortazavi, M., Watanabe, M., & Schaefer, A. (2017). Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task. Biological Psychology, 128, 11-20. https://doi.org/https://doi.org/10.1016/j.biopsycho.2017.06.004

Gorin, A., Nedelko, A., Kosonogov, V., Vakhviyainen, M., Tugin, S., Moiseeva, V., Klucharev, V., & Shestakova, A. (2022). N400 correlates brand associations. Journal of Economic Psychology, 90, 102506.

https://doi.org/https://doi.org/10.1016/j.joep.2022.102506

González-Morales, A. (2020). Right evaluation of marketing stimuli with neuroscience. An electroencephalography experiment. Computers in Human Behavior Reports, 2, 100030.

https://doi.org/https://doi.org/10.1016/j.chbr.2020.100030

Gómez-Carmona, D., Marín-Dueñas, P. P., Tenorio, R. C., Domínguez, C. S., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2022). Environmental concern as a moderator of information processing: A fMRI study. Journal of Cleaner Production, 369, 133306.

https://doi.org/https://doi.org/10.1016/j.jclepro.2022.133306

García-Madariaga, J., Blasco López, M.-F., Burgos, I. M., & Virto, N. R. (2019). Do isolated packaging variables influence consumers' attention and preferences? Physiology & Behavior, 200, 96-103.

https://doi.org/https://doi.org/10.1016/j.physbeh.2018.04.030

Golnar-Nik, P., Farashi, S., & Safari, M.-S. (2019). The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study. Physiology & Behavior, 207, 90-98.

https://doi.org/https://doi.org/10.1016/j.physbeh.2019.04.025

Gountas, J., Gountas, S., Ciorciari, J., & Sharma, P. (2019). Looking beyond traditional measures of advertising impact: Using neuroscientific methods to evaluate social marketing messages. Journal of Business Research, 105, 121-135.

https://doi.org/https://doi.org/10.1016/j.jbusres.2019.07.011

Gordon, Ross and Joseph Ciorciari (2017), “Social Marketing Research and Cognitive Neuroscience,” in K. Kubacki and S. Rundle-Thiele (Eds.), Formative Research in Social Marketing: Innovative Methods to Gain Consumer Insights. (pp. 145-63), Springer DOI:10.1016/j.jbusres.2019.07.011

Gordon R., Ciorciari, J., van Laer, T (2018), “Using EEG to Examine the Role of Attention, Working Memory, Emotion, and Imagination in Narrative Transportation”, European Journal of Marketing, 52 (1/2), 92-117.

 http://dx.doi.org/10.2139/ssrn.2892967

Hakim, A., Klorfeld, S., Sela, T., Friedman, D., Shabat-Simon, M., & Levy, D. J. (2021). Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning. International Journal of Research in Marketing, 38(3), 770-791.

https://doi.org/https://doi.org/10.1016/j.ijresmar.2020.10.005

Harris J, Ciorciari J, Gountas J (2018a), “Consumer Neuroscience for Marketing Researchers,” Journal of Consumer Behaviour, 17 (3), 239-252.

https://doi.org/10.1002/cb.1710

Harris J, Ciorciari J, Gountas J (2018b), “Public Health Social Media Communications and Consumer Neuroscience”, Cogent Psychology, 5 (1), 1434058.

https://doi.org/10.1080/23311908.2018.1434058

Hamelin, N., El Moujahid, O., and Thaichon, P. (2017). Emotion and advertising effectiveness: a novel facial expression analysis approach. J. Retail. Cons. Serv.36, 103–111.

https://doi.org/10.1016/j.jretconser.2017.01.001

Hsu, M., and Yoon, C. (2015). The neuroscience of consumer choice. Curr. Opin. Behav. Sci. 5, 116–121. https://doi.org/10.1016/j.cobeha.2015.09.005

Hsu, L., & Chen, Y.-J. (2020). Neuromarketing, subliminal advertising, and hotel selection: An EEG study. Australasian Marketing Journal (AMJ), 28(4), 200-208.

https://doi.org/https://doi.org/10.1016/j.ausmj.2020.04.009

Jai, T.-M., Fang, D., Bao, F. S., James, R. N., Chen, T., & Cai, W. (2021). Seeing It Is Like Touching It: Unraveling the Effective Product Presentations on Online Apparel Purchase Decisions and Brain Activity (An fMRI Study). Journal of Interactive Marketing, 53, 66-79. https://doi.org/https://doi.org/10.1016/j.intmar.2020.04.005

Jing, K., Qi, M., Mei, Y., & Chen, L. (2022). The impact of empathy with nature on green purchase behavior: An ERP study. Neuroscience Letters, 784, 136745.

https://doi.org/https://doi.org/10.1016/j.neulet.2022.136745

Kalaganis FP, Georgiadis K, Oikonomou VP, Laskaris NA, Nikolopoulos S and Kompatsiaris I (2021) Unlocking the Subconscious Consumer Bias: A Survey on the Past, Present, and Future of Hybrid EEG Schemes in Neuromarketing.Front. Neurosurgeon. 2:672982. doi 10.3389/fnrgo.2021.672982

Kardes, F., Cronley, M. and Cline, T., Consumer Behavior, Mason, OH, South-Western Cengage, 2011 p.7

https://b2n.ir/x97589

  Kardes, F., Cronley, M. and Cline, T., Consumer Behavior, Mason, OH, South-Western Cengage, 2011 p.9.

https://b2n.ir/x97589

Khan, A., & Rasool, S. (2022). Game-induced emotion analysis using electroencephalography. Computers in Biology and Medicine, 145, 105441.

https://doi.org/https://doi.org/10.1016/j.compbiomed.2022.105441

Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40(9), 3803-3812.

https://doi.org/10.1016/j.eswa.2012.12.095

Koc, E., & Boz, H. (2014). Psychoneurobiochemistry of tourism marketing. Tourism Management, 44, 140-148.

https://doi.org/https://doi.org/10.1016/j.tourman.2014.03.002

Kohanzahedani, B., Hassanpour Qorughchi, E., & Mirabi, V. R. (2019). Providing a Comprehensive Branding Pattern for Organic Product with a Sensory Marketing Approach. Journal of System Management, 5(4), 125-136.

DOI: 20.1001.1.23222301.2019.5.4.11.8

Kumar, S., Yadava, M., & Roy, P. P. (2019). Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Information Fusion, 52, 41-52. https://doi.org/https://doi.org/10.1016/j.inffus.2018.11.001

Klaib, A.F.; Alsrehin, N.O.; Melhem, W.Y.; Bashtawi, H.O.; Magableh, A.A. Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Syst. Appl. 2021, 166, 114037.

 DOI:10.1016/J.ESWA.2020.114037

Lu, Y., Papagiannidis, S., & Amananos, E. (2018). Internet of Things: A systematic review of the business literature from the user and organizational perspectives. Technological Forecasting & Social Change, 136, 285–297.

DOI: 10.1016/j.techfore.2018.01.022

Lim, W. M. (2018). Demystifying neuromarketing. Journal of Business Research, 91, 205-220.

https://doi.org/https://doi.org/10.1016/j.jbusres.2018.05.036

Lim, Weng M. (2018), “Demystifying neuromarketing”, Journal of Business Research, 91 (1), 205-220.

https://doi.org/10.1016/j.jbusres.2018.05.036

Lee, Nick, Amanda J. Broderick, and Laura Chamberlain (2007), “What is ‘Neuromarketing’? A discussion and agenda for future research?” International Journal of Psychophysiology, 63 (2), 199-204.

https://doi.org/10.1016/j.ijpsycho.2006.03.007

Lee, E.-J. (2016). Empathy can increase customer equity related to pro-social brands. Journal of Business Research, 69(9), 3748-3754. https://doi.org/https://doi.org/10.1016/j.jbusres.2015.05.018

Lee, N., Broderick, A. J., & Chamberlain, L. (2007). What is ‘neuromarketing’? A discussion and agenda for future research. International Journal of Psychophysiology, 63, 199–204. https://doi.org/10.1016/j.ijpsycho.2006.03.007

Levallois, C., Clithero, J., Wouters, P., Smidts, A., and Huettel, S. (2012). Translating upwards: linking the neural and social sciences via neuroeconomics. Nat. Rev. Neurosci. 13, 789–797. https://doi.org/10.1038/nrn3354

Lajante, M., & Ladhari, R. (2019). The promise and perils of the peripheral psychophysiology of emotion in retailing and consumer services. Journal of Retailing and Consumer Services, 50, 305-313.

https://doi.org/https://doi.org/10.1016/j.jretconser.2018.07.005

Lee, N., Brandes, L., Chamberlain, L., and Senior, C. (2017). This is your brain on neuromarketing: reflections on a decade of research. J. Market. Manage. 33, 878–892. https://doi.org/10.1080/0267257X.2017.1327249

Liu, J., Mo, Z., & Gao, H. (2021). The effect of reviewer’s review volumes on online consumer reviews’ perceived usefulness: An event-related potential study. Neuroscience Letters, 762, 136139.

https://doi.org/10.1016/j.neulet.2021.136139

Mashrur, F. R., Rahman, K. M., Miya, M. T. I., Vaidyanathan, R., Anwar, S. F., Sarker, F., & Mamun, K. A. (2022). An intelligent neuromarketing system for predicting consumers’ future choices from electroencephalography signals. Physiology & Behavior, 253, 113847. https://doi.org/https://doi.org/10.1016/j.physbeh.2022.113847

Ma, Q., Wang, M., & Da, Q. (2021). The effects of brand familiarity and product category in brand extension: An ERP study. Neuroscience Research, 169, 48-56. https://doi.org/https://doi.org/10.1016/j.neures.2020.06.010

Madan, C. R. (2010). Neuromarketing: the next step in market research. Eureka, 1(1), 34–42. https://doi.org/10.29173/eureka7786

Marikyan, D., Papagiannidis, S., & Amananos, E. (2019). A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 138, 139–154.

DOI: 10.1016/j.techfore.2018.08.015

Mashrur, F. R., Rahman, K. M., Miya, M. T. I., Vaidyanathan, R., Anwar, S. F., Sarker, F., & Mamun, K. A. (2022). An intelligent neuromarketing system for predicting consumers’ future choices from electroencephalography signals. Physiology & Behavior, 253, 113847. https://doi.org/https://doi.org/10.1016/j.physbeh.2022.113847

Mateusz, P., & Kesra, N. (2020). Cognitive neuroscience in the design process of social advertising. Procedia Computer Science, 176, 2959-2968. https://doi.org/https://doi.org/10.1016/j.procs.2020.09.207

Meyerding, S. G. H., & Mehlhose, C. M. (2020). Can neuromarketing add value to traditional marketing research? An exemplary experiment with functional near-infrared spectroscopy (fNIRS). Journal of Business Research, 107, 172-185. https://doi.org/https://doi.org/10.1016/j.jbusres.2018.10.052

Montazeribarforoushi, S., Keshavarzsaleh, A., and Ramsøy, T. Z. (2017). On the hierarchy of choice: an applied neuroscience perspective on the AIDA model. Cogent Psychol. 4, 1–23. https://doi.org/10.1080/23311908.2017.1363343

Murtazina, M. S., & Avdeenko, T. V. (2021). The constructing of cognitive functions ontology. Procedia Computer Science, 186, 595-602. https://doi.org/https://doi.org/10.1016/j.procs.2021.04.181

Mulrow, C. D. (1994). Systematic review-rationale for systemic reviews. British Medical Journal. 309, 597–599

DOI: 10.1136/bmj.309.6954.597

Niedziela, M. M., & Ambroze, K. (2021). The future of consumer neuroscience in food research. Food Quality and Preference, 92, 104124. https://doi.org/https://doi.org/10.1016/j.foodqual.2020.104124

Niedermeyer E.; da Silva F.L. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins. ISBN 978-0-7817-5126-1

Nomura, T., & Mitsukura, Y. (2015). EEG-Based Detection of TV Commercials Effects. Procedia Computer Science, 60, 131-140. https://doi.org/https://doi.org/10.1016/j.procs.2015.08.112

Pereira, T., & Oliveira, A. (2021). The usefulness of the averaging rule in consumer studies: A partial replication with an eye at neuroeconomics. European Review of Applied Psychology, 71(5), 100718. https://doi.org/https://doi.org/10.1016/j.erap.2021.100718

Plassmann, H., and Karmarkar, U. R. (2015). “Consumer neuroscience: revealing meaningful relationships between brain and consumer behavior,” in The Cambridge Handbook of Consumer Psychology, eds M. I. Norton, D. D. Rucker, and C. Lamberton (Cambridge: Cambridge University Press), 152–179. https://doi.org/10.1017/CBO9781107706552.006

Ramos, M. M. (2004). Change on the logistics management style through performance indicators; a case study. International Journal of Logistics Research and Applications. 7(4), 313–324.

DOI:10.1080/13675560412331330421

Russo, V., Ciceri, A., Chiappa, M., Giulini, F., Marzari, L., Mauri, M., et al. (2015). Neuromarketing, comunicazione e comportamenti di consumo. Principi, strumenti. Milan: Franco Angeli.

https://b2n.ir/m94708

Ramsøy, T. Z. (2015). An Introduction to Consumer Neuroscience & Neuromarketing. Neurons Inc. https://b2n.ir/b28179

Sanei, S., and Chambers, J. A. (2007). EEG Signal Processing. Chichester: Wiley.

 https://b2n.ir/k19537

Sargent, A., Watson, J., Ye, H., Suri, R., and Ayaz, H. (2020). Neuroergonomic assessment of hot beverage preparation and consumption: an EEG and EDA study. Front. Hum. Neurosci. 14:175. https://doi.org/https://doi.org/10.3389/fnhum.2020.00175

Sassatelli, R., Consumer Culture: History, Theory, and Politics, Sage, 2007, p. 10

DOI: https://doi.org/10.4135/9781446212684

Sebastian, V. (2014). Neuromarketing and Neuroethics. Procedia - Social and Behavioral Sciences, 127, 763-768. https://doi.org/https://doi.org/10.1016/j.sbspro.2014.03.351

Słupińska, K., Duda, J., & Biercewicz, K. (2021). Planning an experiment in a virtual environment reality as a place of research on human behavior using methods of neuroscience measurement – bibliometric analysis and methodological approach. Procedia Computer Science, 192, 3123-3133. https://doi.org/https://doi.org/10.1016/j.procs.2021.09.085

Songsamoe, S., Saengwong-ngam, R., Koomhin, P., and Matan, N. (2019). Understanding consumer physiological and emotional responses to food products using electroencephalography (EEG). Trends Food Sci. Technol. 93, 167–173.

 https://doi.org/10.1016/j.tifs.2019.09.018

Tarannum Zaki, Muhammad Nazrul Islam,(2021) Neurological and physiological measures to evaluate the usability and user experience (UX) of information systems: A systematic literature review, computer Science Review, Volume 40.

https://doi.org/10.1016/j.cosrev.2021.100375

Telpaz, Ariel, Ryan Webb, and Dino J. Levy (2015), “Using EEG to Predict Consumers’ Future Choices,” Journal of Marketing Research, 52 (4), 511-29.

https://doi.org/10.1509/jmr.13.0564

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge using systematic review. British Journal of Management, 14, 207–222.

https://doi.org/10.1111/1467-8551.00375

Uhm, J.-P., Lee, H.-W., & Han, J.-W. (2020). Creating a sense of presence in a virtual reality experience: Impact on neurophysiological arousal and attitude towards a winter sport. Sport Management Review, 23(4), 588-600. https://doi.org/https://doi.org/10.1016/j.smr.2019.10.003

Venkatraman, Vinod, Angelika Dimoka, Paul A. Pavlou, Khoi Vo, William Hampton, Bryan Bollinger, Hal E. Hershfield, Masakazu Ishihara, and Russell S. Winer (2015), “Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling,” Journal of Marketing Research, 52 (4), 436-52.

https://doi.org/10.1509/jmr.13.0593

Vecchiato, Giovanni, Patrizia Cherubino, Arianna Trettel, and Fabio Babiloni (2013), Neuroelectrical brain imaging tools for the study of the efficacy of TV advertising stimuli and their application to Neuromarketing. Heidelberg: Springer. ISBN : 978-3-642-38063-1

Vecchiato, G., Babiloni, F., Astolfi, L., Toppi, J., Cherubino, P., Dai, J., et al. (2011). “Enhance of theta EEG spectral activity related to the memorization of commercial advertisings in Chinese and Italian subjects,” in Proceedings–2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011 (Shanghai), 1491–1494. https://doi.org/10.1109/BMEI.2011.6098615

Vlăsceanu, Sebastian (2014). "New directions in understanding the decision-making process: neuroeconomics and neuromarketing". 17: 758–762.

DOI:10.1016/j.sbspro.2014.03.350

Verhulst, N., Vermeir, I., Slabbinck, H., Larivière, B., Mauri, M., & Russo, V. (2020). A neurophysiological exploration of the dynamic nature of emotions during the customer experience. Journal of Retailing and Consumer Services, 57, 102217. https://doi.org/https://doi.org/10.1016/j.jretconser.2020.102217

Wajid, A., Raziq, M. M., Ahmed, Q. M., & Ahmad, M. (2021). Observing viewers’ self-reported and neurophysiological responses to message appeal in social media advertisements. Journal of Retailing and Consumer Services, 59, 102373. https://doi.org/https://doi.org/10.1016/j.jretconser.2020.102373

Watanuki, S., & Akama, H. (2022). Neural substrates of brand equity: applying a quantitative meta-analytical method for neuroimage studies. Heliyon, 8(6), e09702. https://doi.org/https://doi.org/10.1016/j.heliyon.2022.e09702

Wang, ., Zhao, L., Vilela, A., & Lim, M. (2019). The evolution of Industrial Management & Data Systems over the past 25 years. A bibliometric overview. Industrial Management & Data Systems, 119(1), 2–34.

DOI:10.1108/IMDS-11-2018-0506

Wajid, A., Raziq, M. M., Ahmed, Q. M., & Ahmad, M. (2021). Observing viewers’ self-reported and neurophysiological responses to message appeal in social media advertisements. Journal of Retailing and Consumer Services, 59, 102373. https://doi.org/https://doi.org/10.1016/j.jretconser.2020.102373

Wang, C.-C., & Hsu, M.-C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912-923. https://doi.org/https://doi.org/10.1016/j.im.2014.05.010

Yadegari Taheri, T., Vakil Alroaia, Y., Faezi Razi, F., & Heydariyeh, S. (2021). Identification and Prioritization of Organizational Intelligence Criteria in Production Cooperatives of Iran. Journal of System Management, 7(4), 205-227.

doi:https://doi.org/10.30495/jsm.2021.1942062.1531

Yılmaz, B., Korkmaz, S., Arslan, D. B., Güngör, E., & Asyalı, M. H. (2014). Like/dislike analysis using EEG: Determination of most discriminative channels and frequencies. Computer Methods and Programs in Biomedicine, 113(2), 705-713. https://doi.org/https://doi.org/10.1016/j.cmpb.2013.11.010

Yu, X., Liu, T., He, L., & Li, Y. (2022). Micro-foundations of strategic decision-making in family business organizations: A cognitive neuroscience perspective. Long Range Planning, 102198. https://doi.org/https://doi.org/10.1016/j.lrp.2022.102198

Yen, C., & Chiang, M.-C. (2021). Examining the effect of online advertisement cues on human responses using eye-tracking, EEG, and MRI. Behavioural Brain Research, 402, 113128. https://doi.org/https://doi.org/10.1016/j.bbr.2021.113128

Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472.https://doi.org/10.1177/1094428114562629

Zhao, R., & Zhong, S. (2015). Carbon labeling influences on consumers’ behavior: A system dynamics approach. Ecological Indicators, 51, 98-106. https://doi.org/10.1016/j.ecolind.2014.08.030

Zhao, M., & Wang, X. (2021). Perception value of product-service systems: Neural effects of service experience and customer knowledge. Journal of Retailing and Consumer Services, 62,102617.https://doi.org/10.1016/j.jretconser.2021.102617

Zhao, M. (2022). The impact of cognitive conflict on product-service system value cocreation: An event-related potential perspective. Journal of Cleaner Production, 331, 129987. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.129987

Zhang, J., & Lee, E.-J. (2022). “Two Rivers” brain map for social media marketing: Reward and information value drivers of SNS consumer engagement. Journal of Business Research, 149, 494-505. https://doi.org/https://doi.org/10.1016/j.jbusres.2022.04.022

Zhang, J., Yun, J. H., & Lee, E.-J. (2021). Brain buzz for Facebook? Neural indicators of SNS content engagement. Journal of Business Research, 130, 444-452. https://doi.org/https://doi.org/10.1016/j.jbusres.2020.01.029

Zubair, M., Wang, X., Iqbal, S., Awais, M., & Wang, R. (2020). Attentional and emotional brain response to message framing in the context of green marketing. Heliyon, 6(9), e04912. https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e04912