Using Event-Related Potentials (ERP) to identify the purchase intention of a consumer for familiar brands
DOI:
https://doi.org/10.31117/neuroscirn.v5i4.163Keywords:
Consumer Behavior, Electroencephalography (EEG), Event-Related Potentials (ERP), Unconscious Mental ProcessesAbstract
Several neurological processes are undergoing on a conscious and subconscious level every time a consumer likes or dislikes a product. There is presently significant research in Consumer Neuroscience based on consumer behaviour and understanding of these processes. In this study, we have used Electroencephalography (EEG) and Event-Related Potentials (ERP) to capture consumer responses to highly familiar product images. EEG analysed from the 27 participants was used to extract P1, N1, P300, N400 and Late Posterior components. The analysis showed that the early ERP components viz., P1, N1 and P300 can differentiate between consumer liking and disliking of products. In contrast, the late ERP components N400 and Late Posterior components could not differentiate in the highly familiar product category. The results indicate that after continuous exposure, consumer preference towards highly-familiar products occurs as a part of automatic, unconscious mental processes irrespective of the product properties. Further research in this direction can test for the transference of consumer preference: from a conscious mental process to a subconscious mental process due to excessive and continuous product exposure and marketing repetition. Our study demonstrates that consumer behaviour in response to highly-familiar products can be classified using early ERP components only.
References
Adîr, V., Adîr, G., & Pascu, N.E. (2014). How to design a logo. Procedia - Social and Behavioral Sciences, 122, 140–144. https://doi.org/10.1016/j.sbspro.2014.01.1316
Alsharif, A. H., Md Salleh N.Z., Wan Ahmad W.A., & Khraiwish A. (2022). Biomedical technology in studying consumers' subconscious behavior. International Journal of Online and Biomedical Engineering (IJOE), 18(08), 98–114. https://doi.org/10.3991/ijoe.v18i08.31959
Alsharif, A. H., Md Salleh, N. Z., & Baharun, R. (2021a). Neuromarketing: The popularity of the brain-imaging and physiological tools. Neuroscience Research Notes, 3(5), 13–22. https://doi.org/10.31117/neuroscirn.v3i5.80
Alsharif, A. H., Md Salleh, N. Z., & Baharun, R. (2021b). Neuromarketing: Marketing research in the new millennium. Neuroscience Research Notes, 4(3), 27–35. https://doi.org/10.31117/neuroscirn.v4i3.79
Bleich, A., Attias, J., & Furman, V. (1996). Effect of repeated visual traumatic stimuli on the event related P3 brain potential in post-traumatic stress disorder. International Journal of Neuroscience, 85(1-2), 45–55. https://doi.org/10.3109/00207459608986350
Chen, P., Qiu, J., Li, H., & Zhang, Q. (2009). Spatiotemporal cortical activation underlying dilemma decision-making: An event-related potential study. Biological Psychology, 82(2), 111–115. https://doi.org/10.1016/j.biopsycho.2009.06.007
Choi, C., Joe, S. J., & Mattila, A.S. (2017). Reference price and its asymmetric effects on price evaluations: the moderating role of gender. Cornell Hospitality Quarterly, 59(2), 189–194. https://doi.org/10.1177/1938965517719266
Coll, M.P., Grégoire, M., Prkachin, K.M., & Jackson, P. (2016). Repeated exposure to vicarious pain alters electrocortical processing of pain expressions. Experimental Brain Research, 234(9), 2677–2686. https://doi.org/10.1007/s00221-016-4671-z
Dang, A., & Nichols, B.S. (2022). Consumer response to positive nutrients on the facts up front (FUF) label: A comparison between healthy and unhealthy foods and the role of nutrition motivation. Journal of Marketing Theory and Practice, 1–20. https://doi.org/10.1080/10696679.2021.2020662
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
Han, S., Liu, S., Gan, Y., Xu, Q., Xu, P., Luo, Y., & Zhang, L. (2020). Repeated exposure makes attractive faces more attractive: Neural responses in facial attractiveness judgement. Neuropsychologia, 139, 107365. https://doi.org/10.1016/j.neuropsychologia.2020.107365
Herr, P.M., & Page, C.M. (2004). Asymmetric association of liking and disliking judgments: so what's not to like? Journal of Consumer Research, 30(4), 588–601. https://doi.org/10.1086/380291
Hillyard, S.A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences, 95(3), 781–787. https://doi.org/10.1073/pnas.95.3.781
JASP, & JASP Team. (2019). JASP. [Computer Software].
Jiang, Y., Hong, J., Li, X. L., & Qu, J. (2014). Analysis on visual ergonomics of instrument display system through event-related-potential. IEEE Xplore, 35–39. https://doi.org/10.1109/CogSIMA.2014.6816537
Kocher, M. G., Schudy, S., & Spantig, L. (2018). I lie? We lie! Why? Experimental evidence on a dishonesty shift in groups. Management Science, 64(9), 3995–4008. https://doi.org/10.1287/mnsc.2017.2800
Krishna, A., & Schwarz, N. (2014). Sensory marketing, embodiment, and grounded cognition: A review and introduction. Journal of Consumer Psychology, 24(2), 159–168. https://doi.org/10.1016/j.jcps.2013.12.006
Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique (COGNITIVE NEUROSCIENCE) (1st ed.). MIT Press.
Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience, 8, 213. https://doi.org/10.3389/fnhum.2014.00213
Martin, L.E., & Potts, G.F. (2009). Impulsivity in decision-making: An event-related potential investigation. Personality and Individual Differences, 46(3), 303–308. https://doi.org/10.1016/j.paid.2008.10.019
Ozkara, B.Y., & Bagozzi, R. (2021). The use of event related potentials brain methods in the study of conscious and unconscious consumer decision making processes. Journal of Retailing and Consumer Services, 58, 102202. https://doi.org/10.1016/j.jretconser.2020.102202
Shen, Y., Shan, W., & Luan, J. (2018). Influence of aggregated ratings on purchase decisions: an event-related potential study. European Journal of Marketing, 52(1/2), 147–158. https://doi.org/10.1108/ejm-12-2016-0871
Williams, N.S., McArthur, G.M., & Badcock, N.A. (2021). It's all about time: precision and accuracy of Emotiv event-marking for ERP research. PeerJ, 9, e10700. https://doi.org/10.7717/peerj.10700
Winkler, I., Brandl, S., Horn, F., Waldburger, E., Allefeld, C., & Tangermann, M. (2014). Robust artifactual independent component classification for BCI practitioners. Journal of Neural Engineering, 11(3), 035013. https://doi.org/10.1088/1741-2560/11/3/035013
Winkler, I., Haufe, S., & Tangermann, M. (2011). Automatic Classification of artifactual ICA-components for artifact removal in EEG signals. Behavioral and Brain Functions, 7(1), 30. https://doi.org/10.1186/1744-9081-7-30
Woods, D.L., Wyma, J.M., Yund, E.W., Herron, T.J., & Reed, B. (2015). Factors influencing the latency of simple reaction time. Frontiers in Human Neuroscience, 9, 131. https://doi.org/10.3389/fnhum.2015.00131
Yadava, M., Kumar, P., Saini, R., Roy, P. P., & Prosad Dogra, D. (2017). Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications, 76(18), 19087–19111. https://doi.org/10.1007/s11042-017-4580-6
Downloads
Additional Files
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2022 Mayur Jartarkar, Kopal Srivastava, Veeky Baths
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The observations and associated materials published or posted by NeurosciRN are licensed by the authors for use and distribution in accord with the Creative Commons Attribution license CC BY-NC 4.0 international, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
