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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.

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