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ANALYZING TURKEY'S PREMIER E-COMMERCE MARKETPLACES BY PREDICTIVE EYE TRACKING METHOD

Yıl 2024, , 82 - 101, 31.12.2024
https://doi.org/10.46238/jobda.1490101

Öz

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.

Etik Beyan

Ethical permission Ethics committee approval, numbered 61351342 February 2023/42 and dated 22/02/2023, was obtained from Uskudar University Ethics Committee for this study.

Destekleyen Kurum

Funding The author(s) stated that there is no financial support linked to the research presented in this publication.

Teşekkür

Acknowledgements The author would like to thank Neurons Company.

Kaynakça

  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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Yıl 2024, , 82 - 101, 31.12.2024
https://doi.org/10.46238/jobda.1490101

Öz

Kaynakça

  • 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
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgi Sistemleri Kullanıcı Deneyimi Tasarımı ve Geliştirme
Bölüm Özgün Bilimsel Makaleler
Yazarlar

Dinçer Atlı 0000-0002-8752-6886

Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 26 Mayıs 2024
Kabul Tarihi 19 Kasım 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Atlı, D. (2024). ANALYZING TURKEY’S PREMIER E-COMMERCE MARKETPLACES BY PREDICTIVE EYE TRACKING METHOD. Journal of Business in The Digital Age, 7(2), 82-101. https://doi.org/10.46238/jobda.1490101

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