IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8546-d861327.html
   My bibliography  Save this article

The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

Author

Listed:
  • Rizwan Raheem Ahmed

    (Faculty of Management Sciences, Indus University, Karachi 75300, Pakistan
    School of Business and Economics, State University of New York, Plattsburg, NY 12901, USA)

  • Dalia Streimikiene

    (Institute of Sport Science and Innovations, Lithuanian Sports University, Sporto g. 6, 44221 Kaunas, Lithuania)

  • Zahid Ali Channar

    (Department of Business Administration, Sindh Madressatul Islam University, Karachi 74000, Pakistan)

  • Hassan Abbas Soomro

    (Department of Business Administration, Sukkur IBA University, Sukkur 65200, Pakistan
    Institute of Business Administration, Aix Marseille University, 13007 Marseille, France)

  • Justas Streimikis

    (Lithuanian Centre for Social Sciences, Institute of Economics and Rural Development, A. Vivulskio g. 4A-13, 03220 Vilnius, Lithuania
    Faculty of Management and Finances, University of Economics and Human Science in Warsaw, Okopowa 59, 01-043 Warsaw, Poland)

  • Grigorios L. Kyriakopoulos

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

Abstract

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.

Suggested Citation

  • Rizwan Raheem Ahmed & Dalia Streimikiene & Zahid Ali Channar & Hassan Abbas Soomro & Justas Streimikis & Grigorios L. Kyriakopoulos, 2022. "The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8546-:d:861327
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8546/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8546/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hubert, Mirja, 2010. "Does neuroeconomics give new impetus to economic and consumer research?," Journal of Economic Psychology, Elsevier, vol. 31(5), pages 812-817, October.
    2. Rizwan Raheem Ahmed & Dalia Streimikiene & Gerhard Berchtold & Jolita Vveinhardt & Zahid Ali Channar & Riaz Hussain Soomro, 2019. "Effectiveness of Online Digital Media Advertising as A Strategic Tool for Building Brand Sustainability: Evidence from FMCGs and Services Sectors of Pakistan," Sustainability, MDPI, vol. 11(12), pages 1-40, June.
    3. Ioanna Lykourentzou & Ioannis Giannoukos & George Mpardis & Vassilis Nikolopoulos & Vassili Loumos, 2009. "Early and dynamic student achievement prediction in e‐learning courses using neural networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 372-380, February.
    4. Hsu, Liwei & Chen, Yen-Jung, 2020. "Neuromarketing, subliminal advertising, and hotel selection: An EEG study," Australasian marketing journal, Elsevier, vol. 28(4), pages 200-208.
    5. Yukie Sano & Hideki Takayasu & Shlomo Havlin & Misako Takayasu, 2019. "Identifying long-term periodic cycles and memories of collective emotion in online social media," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
    6. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    7. Luis Mañas-Viniegra & Ana-Isabel Veloso & Ubaldo Cuesta, 2019. "Fashion Promotion on Instagram with Eye Tracking: Curvy Girl Influencers Versus Fashion Brands in Spain and Portugal," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oleksii Lyulyov & Tetyana Pimonenko & Alfonso Infante-Moro & Aleksy Kwilinski, 2024. "Perception of Artificial Intelligence: GSR Analysis and Face Detection," Virtual Economics, The London Academy of Science and Business, vol. 7(2), pages 7-30, June.
    2. Ahmed H. Alsharif & Junhai Wang & Salmi Mohd Isa & Nor Zafir Md Salleh & Husam Azzawi Dawas & Mohammed H. Alsharif, 2025. "The synergy of neuromarketing and artificial intelligence: A comprehensive literature review in the last decade," Future Business Journal, Springer, vol. 11(1), pages 1-30, December.
    3. Rizwan Raheem Ahmed & Farwa Abbas Soomro & Zahid Ali Channar & Alharthi Rami Hashem E & Hassan Abbas Soomro & Munwar Hussain Pahi & Nor Zafir Md Salleh, 2022. "Relationship between Different Dimensions of Workplace Spirituality and Psychological Well-Being: Measuring Mediation Analysis through Conditional Process Modeling," IJERPH, MDPI, vol. 19(18), pages 1-23, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    2. Ivan De Battista & Franco Curmi & Emanuel Said, 2021. "Influencing Factors Affecting Young People’s Attitude Towards Online Advertising: A Systematic Literature Review," International Review of Management and Marketing, Econjournals, vol. 11(3), pages 58-72.
    3. Elnur Nabivi, 2020. "Implementation of Green Marketing Concept Through Social Media Activities: A Systematic Literature Review," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 2(11), pages 55-67.
    4. Goutte, Stéphane & Klotzner, Klemens & Le, Hoang-Viet & von Mettenheim, Hans-Jörg, 2024. "Forecasting photovoltaic production with neural networks and weather features," Energy Economics, Elsevier, vol. 139(C).
    5. Daugherty, Terry & Hoffman, Ernest & Kennedy, Kathleen, 2016. "Research in reverse: Ad testing using an inductive consumer neuroscience approach," Journal of Business Research, Elsevier, vol. 69(8), pages 3168-3176.
    6. Ana C. Martinez-Levy & Dario Rossi & Giulia Cartocci & Marco Mancini & Gianluca Flumeri & Arianna Trettel & Fabio Babiloni & Patrizia Cherubino, 2022. "Message framing, non-conscious perception and effectiveness in non-profit advertising. Contribution by neuromarketing research," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 19(1), pages 53-75, March.
    7. Casado-Aranda, Luis-Alberto & Sánchez-Fernández, Juan & Ibáñez-Zapata, José-à ngel & Liébana-Cabanillas, F.J., 2020. "How consumer ethnocentrism modulates neural processing of domestic and foreign products: A neuroimaging study," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    8. Muhammed Cavus & Dilum Dissanayake & Margaret Bell, 2025. "Deep-Fuzzy Logic Control for Optimal Energy Management: A Predictive and Adaptive Framework for Grid-Connected Microgrids," Energies, MDPI, vol. 18(4), pages 1-25, February.
    9. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    10. Victor Hugo Wentz & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2022. "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models," Energies, MDPI, vol. 15(7), pages 1-23, March.
    11. Dalibor Dobrilovic & Jasmina Pekez & Eleonora Desnica & Ljiljana Radovanovic & Ivan Palinkas & Milica Mazalica & Luka Djordjević & Sinisa Mihajlovic, 2023. "Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    12. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    13. Mariane Bigarelli Ferreira & Giulihano Luis Feltz Zeni & Guilherme Francisco do Prado & Jovani Taveira Souza & Cassiano Moro Piekarski & Fabio Neves Puglieri, 2025. "Challenges and Opportunities for Sustainability in the Digital Product Lifecycle: A Systematic Literature Review," Sustainability, MDPI, vol. 17(16), pages 1-16, August.
    14. Quanwei, Tan & Guijun, Xue & Wenju, Xie, 2025. "Cakformer: Transformer model for long-term heat load forecasting based on Cauto-correlation and KAN," Energy, Elsevier, vol. 324(C).
    15. Gerhard Raab & Christian Elger & Michael Neuner & Bernd Weber, 2011. "A Neurological Study of Compulsive Buying Behaviour," Journal of Consumer Policy, Springer, vol. 34(4), pages 401-413, December.
    16. Berčík, Jakub & Horská, Elena & Wang, W.Y. Regina & Chen, Ying-Chun, 2015. "How can food retailing benefit from neuromarketing research: a case of various parameters of store illumination and consumer response," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202714, European Association of Agricultural Economists.
    17. Bellido-Jiménez, Juan Antonio & Estévez Gualda, Javier & García-Marín, Amanda Penélope, 2021. "Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions," Applied Energy, Elsevier, vol. 298(C).
    18. Muhammad Tahir Jan & Ahmed Mohamed Hamed Aly Abouzaid & Nadeem & Tashpia Hossain, 2020. "Is Your Purchase Intention Influenced by Irrational Factors?An Investigation of Fashion Industry," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 2(11), pages 16-32.
    19. Solnais, Céline & Andreu-Perez, Javier & Sánchez-Fernández, Juan & Andréu-Abela, Jaime, 2013. "The contribution of neuroscience to consumer research: A conceptual framework and empirical review," Journal of Economic Psychology, Elsevier, vol. 36(C), pages 68-81.
    20. Mohammad Hijji & Tzu-Chia Chen & Muhammad Ayaz & Ali S. Abosinnee & Iskandar Muda & Yury Razoumny & Javad Hatamiafkoueieh, 2023. "Optimization of State of the Art Fuzzy-Based Machine Learning Techniques for Total Dissolved Solids Prediction," Sustainability, MDPI, vol. 15(8), pages 1-23, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8546-:d:861327. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.