jobda Journal of Business in The Digital Age 2651-4737 Berrin ONARAN 10.46238/jobda.1490101 Information Systems User Experience Design and Development Bilgi Sistemleri Kullanıcı Deneyimi Tasarımı ve Geliştirme ANALYZING TURKEY'S PREMIER E-COMMERCE MARKETPLACES BY PREDICTIVE EYE TRACKING METHOD https://orcid.org/0000-0002-8752-6886 Atlı Dinçer Esenyurt Üniversitesi 12 31 2024 7 2 82 101 05 26 2024 11 19 2024 Copyright © 2018, Journal of Business in The Digital Age 2018 Journal of Business in The Digital Age

Artificial intelligence (AI) is a rapidly evolving and intensely debated discipline over the last decade. AI has the potential to impact many industries, including neuromarketing. Today, many scholars and academic studies emphasize AI's enormous marketing opportunities. Likewise, neuromarketing is a rapidly expanding discipline in marketing. Neuromarketing often aims to use neuroscientific ideas and marketing strategies and integrate them into marketing domains. Neuromarketing uses electroencephalography, functional magnetic resonance, eye tracking, galvanic skin response, and facial coding to assess subjects' neurophysiological responses to various stimuli. In this study, an analysis was performed with an eye tracker. Eye tracking is the most widely used neuromarketing technology in market research. Today, predictive eye tracking, or AI-based eye tracking, has started to be used as a tool in the neuromarketing field of artificial intelligence. This framework uses many images from device- and subject-based eye-tracking studies to train complex deep-learning algorithms. These algorithms can better predict people's neuroscientific preferences as more data is fed to them. The accuracy of academic visual saliency prediction models is about 90%, with a small margin of error. However, this is expected to improve over time. This study analyzed five web pages in the coffee machine category of Turkey's leading e-commerce marketplaces, www.amazon.com and www.trendyol.com, with cognitive demand and clarity metrics, using Neurovision software. As a result of the analysis, it was determined that the overall cognitive demand metric score of these marketplaces' web pages was acceptable; the overall clarity metric score had the best score on the scale, and the websites in question had very user-friendly designs.

 Neuromarketing Eye Tracking Artificial Intelligence (AI) Predictive Eye Tracking Artificial Intelligence Based Eye Tracking Funding The author(s) stated that there is no financial support linked to the research presented in this publication. 3M VAS. (2020). 3M Visual Attention Service Validation Study. Afifi, E., & Abdo, U. (2022). Using Eye-Tracking Tools in the Visual Assessment of Architecture. Engineering Research Journal - Faculty of Engineering (Shoubra), 51(3), 163–174. https://doi.org/10.21608/erjsh.2022.252293 Ahmed, R. R., Streimikiene, D., Channar, Z. A., Soomro, H. A., Streimikis, J., & Kyriakopoulos, G. L. (2022). The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation From the Advertising Industry. Sustainability, 14(8546), 1–24. https://doi.org/10.3390/su14148546 Alsakar, N., Abdrabou, Y., Stumpf, S., & Khamis, M. (2023). Investigating Privacy Perceptions and Subjective Acceptance of Eye Tracking on Handheld Mobile Devices. Proceedings of the Acm on Human-Computer Interaction, 7(ETRA), 1–16. https://doi.org/10.1145/3591133 Atlı, D. (2015). New Approach to Marketing: Neuromarketing. In C. Daba-Buzoianu, H. Arslan, & M. A. Icbay (Eds.), Contexual Approaches in Communication (1., pp. 493–505). Peter Lang Publishing, Inc. https://doi.org/10.3726/978-3-653-05967-0 Atlı, D., Kose, S. B., & Sezen, A. N. H. (2018). From The Neuromarketing Perspective: The Role Of Narratives In The Advertising. From The Neuromarketing Perspective: The Role Of Narratives In The Advertising, 1–5. Bajaj, R. (2023). Analysing Applications of Neuromarketing in Efficacy of Programmatic Advertising. Journal of Consumer Behaviour, 23(2), 939–958. https://doi.org/10.1002/cb.2249 Bazzani, A., Ravaioli, S., Trieste, L., Faraguna, U., & Turchetti, G. (2020). Is EEG Suitable for Marketing Research? A Systematic Review. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.594566 Bruce, N. D. B., & Tsotsos, J. K. (2009). Saliency, attention and visual search: An information theoretic approach. Journal of Vision, 9(3). https://doi.org/10.1167/9.3.5 Bulut, Z. A. (2015). Determinants of Repurchase Intention in Online Shopping: a Turkish Consumer’s Perspective. In International Journal of Business and Social Science (Vol. 6, Issue 10). www.ijbssnet.com Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., & Durand, F. (2018). What do different evaluation metrics tell us about saliency models? IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3), 740–757. https://doi.org/10.1109/TPAMI.2018.2829657 Cenizo, C. (2022). Neuromarketing: concept, historical evolution and challenges. Icono14, 20(1), 1–18. https://doi.org/10.7195/ri14.v20i1.1784 Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428 Chowdhury, S. (2024). Role of Neuromarketing and Artificial Intelligence in Futuristic Marketing Approach: An Empirical Study. Journal of Informatics Education and Research, April. https://doi.org/10.52783/jier.v4i2.809 Chygryn, O., Shevchenko, K., & Tuliakov, O. (2024). 2024 Marketing and Management of Innovations. Innovations, 15(2), 2024. https://doi.org/10.21272/mmi.2 Ćosić, D. (2016). Neuromarketing in Market Research. Interdisciplinary Description of Complex Systems, 14(2), 139–147. https://doi.org/10.7906/indecs.14.2.3 Davenport, T. H., Guha, A., Grewal, D., & Breßgott, T. (2019). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0 Fisher, C. E., Chin, L., & Klitzman, R. (2010). Defining neuromarketing: Practices and professional challenges. Harvard Review of Psychiatry, 18(4), 230–237. https://doi.org/10.3109/10673229.2010.496623 Fotini-Rafailia, P. (2021). How Neuromarketing , Artificial Intelligence and Machine Learning can improve Technology Companies and their Marketing Strategy: A food market research case using implicit and explicit techniques (Issue January). University Center of International Programmes of Studies School Of Science And Technology. Frey, M., Nau, M., & Doeller, C. F. (2021). Magnetic resonance-based eye tracking using deep neural networks. Nature Neuroscience, 24(12), 1772–1779. https://doi.org/10.1038/s41593-021-00947-w Garczarek-Bąk, U., Szymkowiak, A., Gaczek, P., & Disterheft, A. (2021). A comparative analysis of neuromarketing methods for brand purchasing predictions among young adults. Journal of Brand Management, 28(2), 171–185. https://doi.org/10.1057/s41262-020-00221-7 Gheorghe, C.-M., Purcărea, V. L., & Gheorghe, I. R. (2023). Using eye-tracking technology in Neuromarketing. Romanian Journal of Ophthalmology, 67(1), 2–6. https://doi.org/10.22336/rjo.2023.2 Gordieiev, O., Kharchenko, V., Illiashenko, O., Morozova, O., & Gasanov, M. (2021). Concept of Using Eye Tracking Technology to Assess and Ensure Cybersecurity, Functional Safety and Usability. International Journal of Safety and Security Engineering, 11(4), 361–367. https://doi.org/10.18280/ijsse.110409 Greguš, Ľ. (2023). Opinion Balance of News in the Time of Mistrust in Media and Democratic Institutions. 133–142. https://doi.org/10.34135/mmidentity-2023-13 Hagestedt, I., Backes, M., & Bulling, A. (2020). Adversarial Attacks on Classifiers for Eye-Based User Modelling. https://doi.org/10.1145/3379157.3390511 Henderson, J. M., & Hollingworth, A. (2007). High-level scene perception. Annual Review of Psychology, 58, 243–271. https://doi.org/10.1146/annurev.psych.58.110405.085003 Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Weijer, J. van de. (2011). Eye Tracking A Comprehensive Guide to Methods and Measures (1., Issue 1). Oxford University Press Inc. Hubert, M., & Kenning, P. (2008). A current overview of consumer neuroscience. Journal of Consumer Behaviour, 7(4‐5), 272–292. https://doi.org/10.1002/cb.251 Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3), 194–203. https://doi.org/10.1038/35058500 Joseph, A. W., & Murugesh, R. (2020). Potential Eye Tracking Metrics and Indicators to Measure Cognitive Load in Human-Computer Interaction Research. Journal of Scientific Research, 64(01), 168–175. https://doi.org/10.37398/jsr.2020.640137 Juddk, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. Proceedings of the IEEE International Conference on Computer Vision, Iccv, 2106–2113. https://doi.org/10.1109/ICCV.2009.5459462 Kimery, K. M., & Mccord, M. (2002). Third-Party Assurances: Mapping the Road to Trust in E-retailing. The Journal of Information Technology Theory and Application, 4(2), 63–82. King, A. J., Bol, N., Cummins, R. G., & John, K. K. (2019a). Improving Visual Behavior Research in Communication Science: An Overview, Review, and Reporting Recommendations for Using Eye-Tracking Methods. Communication Methods and Measures, 13(3), 149–177. https://doi.org/10.1080/19312458.2018.1558194 King, A. J., Bol, N., Cummins, R. G., & John, K. K. (2019b). Improving Visual Behavior Research in Communication Science: An Overview, Review, and Reporting Recommendations for Using Eye-Tracking Methods. Communication Methods and Measures, 13(3), 149–177. https://doi.org/10.1080/19312458.2018.1558194 Klaib, A. F., Alsrehin, N. O., Melhem, W. Y., Bashtawi, H. O., & Magableh, A. A. (2021). Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Systems with Applications, 166(September 2020), 114037. https://doi.org/10.1016/j.eswa.2020.114037 Koch, M., Kurzhals, K., Burch, M., & Weiskopf, D. (2022). Visualization Psychology for Eye Tracking Evaluation. https://doi.org/10.48550/arxiv.2204.12860 Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for Humanity. Wiley. Kusá, A. (2023). The Role of Artificial Intelligence in Neuromarketing Research: Insights and Applications. 269–275. https://doi.org/10.34135/mmidentity-2023-27 Lavdas, A. A., Salingaros, N. A., & Sussman, A. (2021). Visual attention software: A new tool for understanding the “subliminal” experience of the built environment. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11136197 Levallois, C., Smidts, A., & Wouters, P. (2019a). The emergence of neuromarketing investigated through online public communications (2002–2008). Business History, 1–41. https://doi.org/10.1080/00076791.2019.1579194 Levallois, C., Smidts, A., & Wouters, P. (2019b). The emergence of neuromarketing investigated through online public communications (2002–2008). Business History, 63(3), 443–466. https://doi.org/10.1080/00076791.2019.1579194 Lim, J. Z., Mountstephens, J., & Teo, J. (2022). Eye-Tracking Feature Extraction for Biometric Machine Learning. Frontiers in Neurorobotics, 15(February). https://doi.org/10.3389/fnbot.2021.796895 Ma, Y. (2023). The Quality Evaluation of Psychometric Scale Reply Base on Eye Tracking. https://doi.org/10.1117/12.2684717 Mashrur, F. R., Rahman, K. M., Miya, M. T. I., Vaidyanathan, R., Anwar, S. F., Sarker, F., & Mamun, K. A. (2022). An intelligent neuromarketing system for predicting consumers’ future choice from electroencephalography signals. Physiology and Behavior, 253(April), 113847. https://doi.org/10.1016/j.physbeh.2022.113847 McLaughlin, L., Bond, R., Hughes, C., McConnell, J., & McFadden, S. (2017). Computing Eye Gaze Metrics for the Automatic Assessment of Radiographer Performance During X-Ray Image Interpretation. International Journal of Medical Informatics, 105, 11–21. https://doi.org/10.1016/j.ijmedinf.2017.03.001 Moya, I., García‐Madariaga, J., & López, M. F. B. (2020). What Can Neuromarketing Tell Us About Food Packaging? Foods, 9(12), 1856. https://doi.org/10.3390/foods9121856 Muna, K. (2023). Eye Tracking Trends in Chemistry Learning: Bibliometric Study 2018-2023 on Google Scholar With VOSviewer and Pivot Table. Journal of Innovative Science Education, 12(3), 309–321. https://doi.org/10.15294/jise.v12i3.76885 Panda, D. (2024). Spatial Attention-Enhanced EEG Analysis for Profiling Consumer Choices. Ieee Access, 12, 13477–13487. https://doi.org/10.1109/access.2024.3355977 Peeters, M. M. M., van Diggelen, J., van den Bosch, K., Bronkhorst, A., Neerincx, M. A., Schraagen, J. M., & Raaijmakers, S. (2021). Hybrid collective intelligence in a human–AI society. AI and Society, 36(1), 217–238. https://doi.org/10.1007/s00146-020-01005-y Prabowo, S. H. W. (2023). The Fierce Competition of Shopee Battlefield Among Generation Z Consumers. 39–47. https://doi.org/10.2991/978-94-6463-302-3_6 Ramanathan, V., Li, F.-F., & Xiao, J. (2010). Eye-tracking assistive annotation for hand-drawn cartoon faces. Proceedings of the 19th International Conference on World Wide Web, 991–1000. https://doi.org/10.1145/1772690.1772788 Ramirez, M., Khalil, M. A., Can, J., & George, K. (2022). Classification of “Like” and “Dislike” Decisions From EEG and fNIRS Signals Using a LSTM Based Deep Learning Network. 2022 IEEE World AI IoT Congress (AIIoT), 252–255. Rawnaque, F. S., Rahman, K. M., Anwar, S. F., Vaidyanathan, R., Chau, T., Sarker, F., & Mamun, K. A. (2020). Technological Advancements and Opportunities in Neuromarketing: A Systematic Review. Brain Informatics. https://doi.org/10.1186/s40708-020-00109-x Riche, N., Duvinage, M., Mancas, M., Gosselin, B., & Dutoit, T. (2013). Saliency and human fixations: State-of-the-art and study of comparison metrics. Proceedings of the IEEE International Conference on Computer Vision, 1153–1160. https://doi.org/10.1109/ICCV.2013.147 Schneider, T., Brenninkmeijer, J., & Woolgar, S. (2022). Enacting the ‘Consuming’ Brain: An Ethnographic Study of Accountability Redistributions in Neuromarketing Practices. The Sociological Review, 70(5), 1025–1043. https://doi.org/10.1177/00380261221092200 Šola, H. M. (2024). Exploring the Untapped Potential of Neuromarketing in Online Learning: Implications and Challenges for the Higher Education Sector in Europe. Behavioral Sciences, 14(2), 80. https://doi.org/10.3390/bs14020080 Solomon, P. R. (2018). Neuromarketing: Applications, Challenges and Promises. Biomedical Journal of Scientific \& Technical Research. https://doi.org/10.26717/bjstr.2018.12.002230 Tang, H., & Pienta, N. J. (2012). Eye-Tracking Study of Complexity in Gas Law Problems. Journal of Chemical Education, 89(8), 988–994. https://doi.org/10.1021/ed200644k Tatler, B. W., Hayhoe, M. M., Land, M. F., & Ballard, D. H. (2011). Eye guidance in natural vision: Reinterpreting salience. Journal of Vision, 11(5), 5. https://doi.org/10.1167/11.5.5 Thontirawong, P., & Chinchanachokchai, S. (2021). Teaching Artificial Intelligence and Machine Learning in Marketing. Marketing Education Review, 31(2), 58–63. https://doi.org/10.1080/10528008.2021.1871849 Tuwirqi, A. A. A. (2024). Eye-Tracking Technology in Dentistry: A Review of Literature. Cureus. https://doi.org/10.7759/cureus.55105 Zammarchi, G., & Conversano, C. (2021). Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis. Vision, 5(4), 56. https://doi.org/10.3390/vision5040056 Zhou, Q., Zuley, M., Guo, Y., Yang, L., Nair, B., Vargo, A., Ghannam, S., Arefan, D., & Wu, S. (2021). A machine and human reader study on AI diagnosis model safety under attacks of adversarial images. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-27577-x