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

Rizwan Raheem Ahmed, Dalia Streimikiene, Zahid Ali Channar, Hassan Abbas Soomro, Justas Streimikis and Grigorios L. Kyriakopoulos
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Rizwan Raheem Ahmed: Faculty of Management Sciences, Indus University, Karachi 75300, Pakistan
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
Justas Streimikis: Lithuanian Centre for Social Sciences, Institute of Economics and Rural Development, A. Vivulskio g. 4A-13, 03220 Vilnius, Lithuania
Grigorios L. Kyriakopoulos: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece

Sustainability, 2022, vol. 14, issue 14, 1-24

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.

Keywords: neuromarketing; artificial neural networks; functional magnetic resonance imaging; electroencephalography; predicted consumer buying behavior (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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