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Comparison of Studies Conducted in the Field of Neuromarketing and Artificial Intelligence Using Bibliometric Method

Year 2024, Volume: 11 Issue: 3, 980 - 1010, 30.09.2024
https://doi.org/10.30798/makuiibf.1416687

Abstract

Abstract
Neuromarketing research focuses on consumer purchase intention, decision-making processes, purchase behavior, brand awareness, brand loyalty, and repeat purchase behavior. In these studies, consumer behavior has been analyzed using neuroscientific methods and tools. The most commonly used tools include Functional Magnetic Resonance Imaging (fMRI), Eye Tracking, Electroencephalogram (EEG), Positron Emission Tomography (PET), Transcranial Magnetic Stimulation (TMS), Magnetoencephalogram (MEG), Steady State Topography (SST), Implicit Association Test (IAT), Facial Electromyography (fEMG), Automatic Face Coding (AFC), Skin Conductance Response (SCR), and other methods for measuring physiological responses. However, the use of these neuroscientific tools is not always possible due to economic constraints and lack of experimental design. Neuroscientific imaging and measurement methods are not preferred in every study due to their high costs and expertise requirements. However, when neuromarketing studies are examined, it is seen that methods such as Eye Tracking, EEG and fMRI are used more widely. These tools contribute to a deeper understanding of consumer behavior. In order to better analyze consumer behavior, it is of great importance to convey marketing stimuli and messages correctly. In the field of marketing, the effect of stimuli conveyed to consumers using the five senses is one of the focal points of neuromarketing. More than one neuroscientific method should be used together to understand consumer intentions, thoughts and purchasing behaviors. In this way, the obtained data can be analyzed more comprehensively and clearer insights can be provided about neuromarketing. The aim of this study is to present a comprehensive assessment of the use of neuroscientific tools by examining the publications in the field of neuromarketing in the Web of Science database between 2010-2024 with bibliometric analysis. The study will address the limitations of not using more than one neuroscientific tool together in neuromarketing research and the inadequacy of analyses supported by artificial intelligence. A more holistic approach will be proposed to address these shortcomings and a guiding resource for future research will be created.

References

  • Abbott, D. (2014). Applied predictive analytics: Principles and techniques for the professional data analyst. Wiley.
  • Al, U., Sezen, U., & Soydal, İ. (2019). Türkiye'nin bilimsel yayınlarının sosyal ağ analizi yöntemiyle değerlendirilmesi. Hacettepe Üniversitesi Proje Raporları No: 110K044.
  • Aldayel, M., Ykhlef, M., & Al-Nafjan, A. (2020). Deep learning for EEG-based preference classification in neuromarketing. Applied Sciences, 10(4), 1525.
  • Aldayel, M., Ykhlef, M., & Al-Nafjan, A. (2021). Recognition of consumer preference by analysis and classification EEG signals. Frontiers in Human Neuroscience, 14, 604639.
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284-292.
  • Bhandari, A. (2020). Neuromarketing Trends and opportunities for companies. In Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience (pp. 82-103). IGI Global.
  • Binodl, S., & Jothi, G. (2020). Customer’s perception towards neuromarketing techniques adopted by Indian brands. Studies in Indian Place Names, 40(12), 1304-1307.
  • Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40.
  • Brenninkmeijer, J., Schneider, T. & Woolgar, S. (2020). Witness and silence in neuromarketing: Managing the gap between science and its application. Science, Technology, & Human Values, 45(1), 62-86.
  • Burgos-Campero, A. & Vargas-Hernandez, J. (2013). Analitical approach to neuromarketing as a business strategy. Procedia - Social and Behavioral Sciences, (99), 517-525.
  • Cannella, J. (2018). Artificial Intelligence in marketing [Unpublished Honors Thesis for Barrett], The Honors College at Arizona State University.
  • Cárdenas, G. (2019). Neuromarketing as an effective tool for education in sales and advertising. Revista Latina de Comunicación Social, (74), 1173-1189.
  • Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B., (2019). Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, (32), 144-157.
  • Clifton R., (2014). Markalar ve markalaşma (Çev. Meral Çiyan Şenerdi), Kültür Yayınları.
  • Conejo, F., Khoo, C., Tanakinjal, G. & Yang, L. (2007). Neuromarketing: Will it revolutionise business? International Journal of Business and Management, 2(6), 72-76.
  • Cosic, D. (2016). Neuromarketing in market research. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 139-147.
  • De Oliveira, J. & Giraldi, J. (2017). What is neuromarketing? A proposal for a broader and more accurate definition. Global Business and Management Research: An International Journal, 9(2), 19-29.
  • Duque-Hurtado, P., Samboni-Rodriguez, V., Castro-Garcia, M., Montoya-Restrepo, L. A., & Montoya-Restrepo, I. A. (2020). Neuromarketing: Its current status and research perspectives. Estudios Gerenciales (Journal of Management and Economics for Iberoamerica), 36(157), 525-539.
  • Elia, G., Margherita, A., & Passiante, G. (2020). Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process. Technological Forecasting and Social Change, 150, 119791.
  • Fortunato, V., Giraldi, J. & De Oliveira, J. (2014). A Review of studies on neuromarketing: Practical results, techniques, contributions and limitations. Journal of Management Research, 6(2), 201-220.
  • Galandi, N., Briesemeister, B. B., Kant, T., & Hagen, D. (2022). Beyond neuromarketing: How neuroscience is penetrating new areas of business. Neuromarketing in Business: Identifying Implicit Purchase Drivers and Leveraging them for Sales, 109-125.
  • Gangadharbatla, H., Bradley, S., & Wise, W. (2013). Psychophysiological responses to background brand placements in video games. Journal of Advertising, 42(2-3), 251-263.
  • 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.
  • Grajdieru, E. (2017). Neuromarketing and its internal marketing applications. Bulletin of the Transilvania University of Brasov. Series V: Economic Sciences, 17-24.
  • Saxon, J. (2017). Why your customers’ attention is the scarcest resource in 2017. IE School of Human
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Sebastian, V. (2014). Neuromarketing and evaluation of cognitive and emotional responses of consumers to marketing stimuli. Procedia - Social and Behavioral Sciences, 127, 753-757.
  • Smart Insights. (n.d.). Artificial Intelligence (AI) for marketing. Smart Insights. https://www.smartinsights.com/tag/artificial-intelligence-ai-for-marketing/
  • Senior, C. & Lee, N. (2008). Editorial: A manifesto for neuromarketing science. Journal of Consumer Behaviour, 7, 263-271.
  • Seranmadevi, R., & Kumar, A. (2019). Experiencing the AI emergence in Indian retail–Early adopters approach, Management Science Letters, 9(1). 33-42.
  • Shukla, S. (2019). Neuromarketing: A change in marketing tools and techniques. International Journal of Business Forecasting and Marketing Intelligence, 5(3), 267-284.
  • Singh, S. (2020). Impact of neuromarketing applications on consumers. Journal of Business and Management, 26(2), 33-52.
  • Somervuori, O., & Ravaja, N. (2013). Purchase behavior and psychophysiological responses to different price levels. Psychology & Marketing, 30(6), 479-489.
  • Songur A. (2022). Aşkın nöroanatomisi. LabMedia Magazine, 13(77), 26-27.
  • Stanton, S., Sinnott-Armstrong, W. & Huettel, S. (2017). Neuromarketing: Ethical implications of its use and potential misuse. Journal of Business Ethics, 144(4), 799-811.
  • Stas, A., Songa, G., Mauri, M., Ciceri, A., Diotallevi, F., Nardone, G. & Russo, V. (2018). Neuromarketing empirical approaches and food choice: A systematic review. Food Research International, 108, 650-664 https://doi.org/10.1016/j.foodres.2017.11.049
  • Statista, (2024). Distribution of advertising spending worldwide in 2023. Statista. https://www.statista.com/statistics/269333/distribution-of-global-advertising-expenditure/
  • Thomas, A. R., Pop, N. A., Iorga, A. M., & Ducu, C. (2017). Ethics and neuromarketing. Implications for Market Research and Business Practice. Springer.
  • Tjepkema, L. (2019). What is artificial intelligence marketing & why is it so powerful. Emarsys. https://emarsys.com/learn/blog/artificial-intelligence-marketing-solutions/
  • Ural, T. (2008). Pazarlamada yeni yaklaşım: Nöropazarlama üzerine kuramsal bir değerlendirme. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 17(2), 421-432.
  • Van Esch, P. & J. Stewart Black (2021), Artificial Intelligence (AI): Revolutionizing digital marketing. Australasian Marketing Journal, 29(3), 199-203.
  • Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., ... & Winer, R. S. (2015). Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. Journal of Marketing Research, 52(4), 436-452.
  • Villegas, F., (2023). Artificial Intelligence customer experience: What is it, pros, cons and best tools. QuestionPro. https://www.questionpro.com/blog/ai-customer-experience/
  • Wilson, R., Gaines, J. & Hill, R. (2008). Neuromarketing and consumer free will. Journal of Consumer Affairs, 42(3), 389-410.
  • Wirtz, J. (2021), Artificial Intelligence in marketing: Bibliometric analysis, topic modeling and research agenda, Journal of Business Research, (124), 389-404.
  • Yağci, M. I., Kuhzady, S., Balik, Z. S., & Öztürk, L. (2018). In search of consumer's black box: a bibliometric analysis of neuromarketing research. Tüketici ve Tüketim Araştırmaları Dergisi, 10(1), 101-134.
  • Yasir, F. & Haq, M.A.U. (2022). Neuromarketing-seeing the unseen: Effect of in-store category artwork, structures and packaging on shopper’s buying behaviors in Pakistan. Journal of Marketing Strategies, 4(2), 227-245.
  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429- 472.
Year 2024, Volume: 11 Issue: 3, 980 - 1010, 30.09.2024
https://doi.org/10.30798/makuiibf.1416687

Abstract

References

  • Abbott, D. (2014). Applied predictive analytics: Principles and techniques for the professional data analyst. Wiley.
  • Al, U., Sezen, U., & Soydal, İ. (2019). Türkiye'nin bilimsel yayınlarının sosyal ağ analizi yöntemiyle değerlendirilmesi. Hacettepe Üniversitesi Proje Raporları No: 110K044.
  • Aldayel, M., Ykhlef, M., & Al-Nafjan, A. (2020). Deep learning for EEG-based preference classification in neuromarketing. Applied Sciences, 10(4), 1525.
  • Aldayel, M., Ykhlef, M., & Al-Nafjan, A. (2021). Recognition of consumer preference by analysis and classification EEG signals. Frontiers in Human Neuroscience, 14, 604639.
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284-292.
  • Bhandari, A. (2020). Neuromarketing Trends and opportunities for companies. In Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience (pp. 82-103). IGI Global.
  • Binodl, S., & Jothi, G. (2020). Customer’s perception towards neuromarketing techniques adopted by Indian brands. Studies in Indian Place Names, 40(12), 1304-1307.
  • Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40.
  • Brenninkmeijer, J., Schneider, T. & Woolgar, S. (2020). Witness and silence in neuromarketing: Managing the gap between science and its application. Science, Technology, & Human Values, 45(1), 62-86.
  • Burgos-Campero, A. & Vargas-Hernandez, J. (2013). Analitical approach to neuromarketing as a business strategy. Procedia - Social and Behavioral Sciences, (99), 517-525.
  • Cannella, J. (2018). Artificial Intelligence in marketing [Unpublished Honors Thesis for Barrett], The Honors College at Arizona State University.
  • Cárdenas, G. (2019). Neuromarketing as an effective tool for education in sales and advertising. Revista Latina de Comunicación Social, (74), 1173-1189.
  • Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B., (2019). Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, (32), 144-157.
  • Clifton R., (2014). Markalar ve markalaşma (Çev. Meral Çiyan Şenerdi), Kültür Yayınları.
  • Conejo, F., Khoo, C., Tanakinjal, G. & Yang, L. (2007). Neuromarketing: Will it revolutionise business? International Journal of Business and Management, 2(6), 72-76.
  • Cosic, D. (2016). Neuromarketing in market research. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 139-147.
  • De Oliveira, J. & Giraldi, J. (2017). What is neuromarketing? A proposal for a broader and more accurate definition. Global Business and Management Research: An International Journal, 9(2), 19-29.
  • Duque-Hurtado, P., Samboni-Rodriguez, V., Castro-Garcia, M., Montoya-Restrepo, L. A., & Montoya-Restrepo, I. A. (2020). Neuromarketing: Its current status and research perspectives. Estudios Gerenciales (Journal of Management and Economics for Iberoamerica), 36(157), 525-539.
  • Elia, G., Margherita, A., & Passiante, G. (2020). Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process. Technological Forecasting and Social Change, 150, 119791.
  • Fortunato, V., Giraldi, J. & De Oliveira, J. (2014). A Review of studies on neuromarketing: Practical results, techniques, contributions and limitations. Journal of Management Research, 6(2), 201-220.
  • Galandi, N., Briesemeister, B. B., Kant, T., & Hagen, D. (2022). Beyond neuromarketing: How neuroscience is penetrating new areas of business. Neuromarketing in Business: Identifying Implicit Purchase Drivers and Leveraging them for Sales, 109-125.
  • Gangadharbatla, H., Bradley, S., & Wise, W. (2013). Psychophysiological responses to background brand placements in video games. Journal of Advertising, 42(2-3), 251-263.
  • 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.
  • Grajdieru, E. (2017). Neuromarketing and its internal marketing applications. Bulletin of the Transilvania University of Brasov. Series V: Economic Sciences, 17-24.
  • Saxon, J. (2017). Why your customers’ attention is the scarcest resource in 2017. IE School of Human
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Sebastian, V. (2014). Neuromarketing and evaluation of cognitive and emotional responses of consumers to marketing stimuli. Procedia - Social and Behavioral Sciences, 127, 753-757.
  • Smart Insights. (n.d.). Artificial Intelligence (AI) for marketing. Smart Insights. https://www.smartinsights.com/tag/artificial-intelligence-ai-for-marketing/
  • Senior, C. & Lee, N. (2008). Editorial: A manifesto for neuromarketing science. Journal of Consumer Behaviour, 7, 263-271.
  • Seranmadevi, R., & Kumar, A. (2019). Experiencing the AI emergence in Indian retail–Early adopters approach, Management Science Letters, 9(1). 33-42.
  • Shukla, S. (2019). Neuromarketing: A change in marketing tools and techniques. International Journal of Business Forecasting and Marketing Intelligence, 5(3), 267-284.
  • Singh, S. (2020). Impact of neuromarketing applications on consumers. Journal of Business and Management, 26(2), 33-52.
  • Somervuori, O., & Ravaja, N. (2013). Purchase behavior and psychophysiological responses to different price levels. Psychology & Marketing, 30(6), 479-489.
  • Songur A. (2022). Aşkın nöroanatomisi. LabMedia Magazine, 13(77), 26-27.
  • Stanton, S., Sinnott-Armstrong, W. & Huettel, S. (2017). Neuromarketing: Ethical implications of its use and potential misuse. Journal of Business Ethics, 144(4), 799-811.
  • Stas, A., Songa, G., Mauri, M., Ciceri, A., Diotallevi, F., Nardone, G. & Russo, V. (2018). Neuromarketing empirical approaches and food choice: A systematic review. Food Research International, 108, 650-664 https://doi.org/10.1016/j.foodres.2017.11.049
  • Statista, (2024). Distribution of advertising spending worldwide in 2023. Statista. https://www.statista.com/statistics/269333/distribution-of-global-advertising-expenditure/
  • Thomas, A. R., Pop, N. A., Iorga, A. M., & Ducu, C. (2017). Ethics and neuromarketing. Implications for Market Research and Business Practice. Springer.
  • Tjepkema, L. (2019). What is artificial intelligence marketing & why is it so powerful. Emarsys. https://emarsys.com/learn/blog/artificial-intelligence-marketing-solutions/
  • Ural, T. (2008). Pazarlamada yeni yaklaşım: Nöropazarlama üzerine kuramsal bir değerlendirme. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 17(2), 421-432.
  • Van Esch, P. & J. Stewart Black (2021), Artificial Intelligence (AI): Revolutionizing digital marketing. Australasian Marketing Journal, 29(3), 199-203.
  • Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., ... & Winer, R. S. (2015). Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. Journal of Marketing Research, 52(4), 436-452.
  • Villegas, F., (2023). Artificial Intelligence customer experience: What is it, pros, cons and best tools. QuestionPro. https://www.questionpro.com/blog/ai-customer-experience/
  • Wilson, R., Gaines, J. & Hill, R. (2008). Neuromarketing and consumer free will. Journal of Consumer Affairs, 42(3), 389-410.
  • Wirtz, J. (2021), Artificial Intelligence in marketing: Bibliometric analysis, topic modeling and research agenda, Journal of Business Research, (124), 389-404.
  • Yağci, M. I., Kuhzady, S., Balik, Z. S., & Öztürk, L. (2018). In search of consumer's black box: a bibliometric analysis of neuromarketing research. Tüketici ve Tüketim Araştırmaları Dergisi, 10(1), 101-134.
  • Yasir, F. & Haq, M.A.U. (2022). Neuromarketing-seeing the unseen: Effect of in-store category artwork, structures and packaging on shopper’s buying behaviors in Pakistan. Journal of Marketing Strategies, 4(2), 227-245.
  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429- 472.
There are 49 citations in total.

Details

Primary Language English
Subjects Neuromarketing
Journal Section Research Articles
Authors

Abdullah Ballı 0000-0003-2689-6610

Publication Date September 30, 2024
Submission Date January 8, 2024
Acceptance Date August 23, 2024
Published in Issue Year 2024 Volume: 11 Issue: 3

Cite

APA Ballı, A. (2024). Comparison of Studies Conducted in the Field of Neuromarketing and Artificial Intelligence Using Bibliometric Method. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(3), 980-1010. https://doi.org/10.30798/makuiibf.1416687