Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Doğuş Yüksel
 -- 1794 2023-11-29 12:22:01 |
2 layout & references
Sirius Huang
 Meta information modification 1794 2023-11-30 02:33:52 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Yüksel, D. Web-Based Eye-Tracking for Neuromarketing. Encyclopedia. Available online: https://encyclopedia.pub/entry/52181 (accessed on 04 May 2024).
Yüksel D. Web-Based Eye-Tracking for Neuromarketing. Encyclopedia. Available at: https://encyclopedia.pub/entry/52181. Accessed May 04, 2024.
Yüksel, Doğuş. "Web-Based Eye-Tracking for Neuromarketing" Encyclopedia, https://encyclopedia.pub/entry/52181 (accessed May 04, 2024).
Yüksel, D. (2023, November 29). Web-Based Eye-Tracking for Neuromarketing. In Encyclopedia. https://encyclopedia.pub/entry/52181
Yüksel, Doğuş. "Web-Based Eye-Tracking for Neuromarketing." Encyclopedia. Web. 29 November, 2023.
Web-Based Eye-Tracking for Neuromarketing
Edit
This entry is adapted from the peer-reviewed paper 10.3390/jtaer18040105

The increasing popularity of neuromarketing has led to the emergence of various measurement methods, such as webcam-based eye-tracking technology. Webcam-based eye-tracking technology is noteworthy not only for its use in laboratories but also for its ability to be applied to participants online in their natural environments through a link. However, the complexity of e-commerce interfaces necessitates high performance in eye-tracking methods.

web-based eye tracking neuromarketing e-commerce human computer interaction lighting conditions

1. Introduction

Visual stimuli constitute the primary source of information from the environment and occupy a broader cognitive space in the brain compared to other sensory inputs [1][2][3][4][5][6]. As a reflection of the intricate workings of the mind, the processing and interpretation of visual data have become significant factors in various aspects today. In this context, visual perception has become a focal point both academically and commercially [7]. Eye tracking is an experimental research method that has emerged at the intersection of psychology, neuroscience, and marketing with the aim of better understanding human behaviors. It involves tracking and recording a user’s eye movements [8]. This technology, which allows the measurement of visual attention [9], enables the examination of where, when, for how long, and in what order a user focuses, as well as the intensity and distribution of this focus [10]. Eye-tracking technology is increasingly being used for purposes such as understanding responses to visual stimuli, evaluating advertising effectiveness, optimizing product designs, understanding consumer behaviors, making marketing strategies more effective and personalized [11], and ensuring customer satisfaction [12] (p. 64), and its usage has been on the rise [13][14]. These studies, which are a critical tool for the analysis of visual attention, can be conducted both in laboratory settings and in natural scenarios [15]. With the increasing interest in eye-tracking technology and its expanding range of applications, the devices and techniques used in this field have also evolved. Eye-tracking technology has evolved over time, offering various options ranging from non-user-friendly contact lenses [16][17][18][19] to wearable devices and video-based methods [20][21][22][23][24][25]. During this process, the cost of eye-tracking technologies has decreased, making these technologies more accessible. Webcam-based eye-tracking technology, in particular, has gained attention due to its low cost, lack of additional hardware requirements, absence of physical contacts, and its ability to be applied anywhere through a simple link. The ability to apply this method anywhere allows participants to take part in the experiment from various positions with natural and artificial lighting sources with the light coming from different directions (from the right, left, and center). When evaluated in conjunction with the information density and complexity of e-commerce interfaces, this situation has raised questions about how the direction and type of lighting affect webcam-based eye-tracking performance.

2. Eye-Tracking in the Field of E-Commerce

The sense of vision serves as a primary conduit for a plethora of information derived from the surrounding environment. One-quarter of the brain’s volume is allocated to visual image processing and its integration. Visual image processing occupies a broader area in the brain compared to other senses, indicating the significance of the sense of vision [1][2][3][4][5][6]. Comprehending the fundamental physiological structure of the eye and the process through which vision is formed is essential for eye-tracking studies [26][27].
The process of image formation begins with the arrival of light signals reflected from objects to the eye. Within the brain, there exist millions of neurons that transform these luminous signals into encoded electrochemical signals. This group of neurons, also known as photoreceptors, constitutes the retina. Continuous movements of the eyeballs allow the light inputs from the object of interest to fall onto this region [4] (p. 13). Contrary to popular belief, eye movements are not smooth. The process of vision involves two distinct eye movements known as saccades and fixations [15]. Saccades are rapid eye movements lasting approximately 20–40 milliseconds (occurring at an average rate of three to four times per second) and are recognized as the swiftest motions within the human body. Fixations, on the other hand, occur following these rapid eye movements, during which the eyes remain relatively stable (approximately 200–500 milliseconds) and focused on the object of interest [28][29][30][31]. Due to the inability of the eye to process the target image with high quality in a single fixation, it necessitates frequent movements. Consequently, most fixations are relatively short, and this duration can vary depending on factors such as the nature of the visual stimulus, the purpose and complexity of the task, the individual capabilities of the observer, and the focal point of attention [32]. During fixations, although perception may appear static, the eye is actually engaged in continuous movements, including tremors, drifts, and microsaccades [33] (pp. 3–13). However, saccades and fixations are the most frequently discussed eye movements, particularly capturing the attention of user experience (UX) researchers in e-commerce and marketing research [31] (p. 12).
Eye tracking is an experimental method wherein the eye movements and gaze position of a user are tracked and recorded over a specific period and task duration, enabling the measurement of visual attention [9]. This method is frequently employed in areas where the focus and distribution of visual attention are crucial [14][27][34]. This method allows for the investigation of where, when, for how long, and in what sequence a user directs his focus, thus making it a versatile research technique across various disciplines [9]. From fundamental studies of perception and memory [34][35][36] to game strategy development [37][38], from marketing and advertising [7] to industrial engineering [39], and from driving behaviors [40] to the professional development of educators [41], eye-tracking studies find application in almost every facet of life. Eye tracking, a tool for the analysis of visual attention, is garnering increasing attention in the relevant literature [9][27][42][43].
The growing interest and significance of eye tracking have been reflected in the variety of devices and techniques developed. The eye-tracking methodology originated with attempts to monitor the pupil externally and evolved into its present form through the development of after images, attachment devices, optical devices, remote devices, electro-oculographic devices, and portable eye-tracking techniques throughout history [20] (pp. 14–28).
Many early-stage eye-tracking techniques employed devices such as contact lenses [16] and electrodes [44], which necessitated physical intervention, making them impractical and discomforting for the user. Later, wearable eye-tracking devices that were less intrusive were developed, but these devices did not achieve the desired practical impact either [18][19]. In contrast, video-based eye-tracking techniques that are more suitable for daily use and do not cause discomfort to the user have begun to be developed [22][23].
Jacob and Karn [45] pointed out that despite the promising advancements in eye-tracking research, the utilization of such technologies had yet to proliferate to its potential level. They attributed this to the limitations related to eye-tracking hardware and software, as well as a lack of knowledge in interpreting the acquired data. In addition to these factors, the nature of the calibration process, requiring meticulous work and the shortage of competent experts in interpreting the acquired data [46] (p. 5), as well as factors like the cost of laboratory equipment and hardware, can be attributed to the limited proliferation of eye-tracking technology to the desired extent [47][48]. Nevertheless, the high-cost hardware and software in eye-tracking technology are gradually being replaced by more affordable alternatives [49]. The development of open-source software for data analysis [50], the production of wearable eye-tracking devices [51], the utilization of web cameras for eye-tracking [21][22][24][48], and the creation of eye-tracking software driven by artificial intelligence technology [52] are among the efforts aimed at making research in this field more accessible and widespread.
Eye tracking, based on the assumption of a direct relationship between where an individual looks and what they are cognitively attending to [31][43][53], can be conducted both in laboratory settings and in users’ natural environments. During the data collection process, users can be instructed to observe a variety of predetermined stimuli, which can be either static or dynamic, living or non-living [15]. Eye-tracking hardware can be categorized into two main groups: desktop-based devices and mobile devices. Desktop-based devices, which are mounted in a fixed location, record users’ eye movements from a stationary and relatively distant position, requiring users to sit in front of a screen. Mobile devices, as the name implies, allow users movement and capture images using wearable eye-tracking devices [25]. Eye-tracking technologies can measure various variables, including the position, number, and duration of fixations, the sequence and speed of saccades, pupil diameter, blink frequency, and many other factors [10].
While traditional marketing methods aim to decipher consumers’ approaches towards specific products and develop suitable sales strategies, they may not fully reflect consumers’ attitudes at the moment of purchase. Misunderstanding or misinterpreting these attitudes could lead to adverse outcomes for businesses, thus leading to an increasing focus on neuromarketing tools [54]. Neuromarketing has introduced a novel area where both cognitive and emotional aspects of consumer behavior can be studied together [8]. Research in this field has shown that individuals might not always express their preferences and intentions clearly, and they might not always be aware of the underlying reasons for their choices. This phenomenon is a crucial factor for both researchers in the field and relevant businesses to understand how the consumer’s brain responds to specific stimuli [55].
The number and variety of commercial stimuli that consumers are exposed to in daily life are increasing. Visual advertisements are among the marketing stimuli that businesses most commonly resort to in order to closely monitor their target consumer groups. From the consumer’s perspective, individuals engage in an information search process throughout their purchasing journey to fulfil various desires and needs, and visual search is the most effective tool in this process [56]. The field of marketing, as both a philosophy and an organizational strategy, centers on achieving customer satisfaction and conducting the most suitable purchasing actions by focusing on these information-searching behaviors [12] (p. 64). As a result, with the increasing significance and scope of visual marketing, the growing utilization of eye-tracking technology in commercial settings has also become an expected progression. It is known that numerous companies such as PepsiCo, Pfizer, P&G, and Unilever have employed this technology to formulate sales strategies in both America and Europe [7]. These developments are further supported by the reduction in the costs associated with eye-tracking tools and research.
With the increasing prevalence of e-commerce, consumers’ shopping experiences have diverged from their experiences in physical stores [13]. There exist numerous studies in the literature that compare physical and virtual stores [57][58][59]. In physical stores, the store atmosphere influences consumer behaviors [60][61][62]. In e-commerce and digital marketing processes, many significant variables used in the store atmosphere become obsolete. Factors that affect the atmosphere like scent, lighting, and ventilation cannot be controlled in the e-commerce process. This aspect sets e-commerce apart from physical stores.
Due to its inherent nature, e-commerce is unable to convey elements such as the scent, taste, and texture of a product to consumers within the current technological infrastructure [63] and, therefore, cannot address consumers’ sensory characteristics. In e-commerce, the most crucial and fundamental variable is the power of visual presentation. These reasons highlight the significance of eye-tracking in the field of e-commerce.

References

  1. Schifferstein, H.N.J. The perceived importance of sensory modalities in product usage: A study of self-reports. Acta Psychol. 2006, 121, 41–64.
  2. Hultén, B.; Broweus, N.; van Dijk, M. What Is Sensory Marketing? Palgrave Macmillan: London, UK, 2009; pp. 1–23.
  3. Wei-Lun, C.; Hsieh-Liang, L. The impact of color traits on corporate branding. Afr. J. Bus. Manag. 2010, 4, 3344–3355.
  4. Zurawicki, L. Neuromarketing: Exploring the Brain of the Consumer; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2010; pp. 12–13.
  5. Krishna, A. An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior. J. Consum. Psychol. 2012, 22, 332–351.
  6. Mounica, M.S.; Manvita, M.; Jyotsna, C.; Amudha, J. Low Cost Eye Gaze Tracker Using Web Camera. In Proceedings of the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 27–29 March 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 79–85.
  7. Wedel, M.; Pieters, R. Eye tracking for visual marketing. Found. Trends Mark. 2008, 1, 231–320.
  8. Morin, C. Neuromarketing: The new science of consumer behavior. Society 2011, 48, 131–135.
  9. Kiefer, P.; Giannopoulos, I.; Raubal, M.; Duchowski, A. Eye tracking for spatial research: Cognition, computation, challenges. Spat. Cogn. Comput. 2017, 17, 1–19.
  10. Smilek, D.; Carriere, J.S.A.; Cheyne, J.A. Out of mind, out of sight: Eye blinking as indicator and embodiment of mind wandering. Psychol. Sci. 2010, 21, 786–789.
  11. Li, C. The placebo effect in web-based personalization. Telemat. Inform. 2019, 44, 101267.
  12. Brugarolas, M.; Martínez-Carrasco, L. The sense of sight. In Sensory and Aroma Marketing; Sendra, E., Carbonell-Barrachina, Á.A., Eds.; Wageningen Academic Publishers: Gelderland, The Netherlands, 2017; pp. 61–92.
  13. Kauppinen-Räisänen, H. Strategic use of colour in brand packaging. Packag. Technol. Sci. 2014, 27, 663–676.
  14. Gómez-Carmona, D.; Cruces-Montes, S.; Marín-Dueñas, P.P.; Serrano-Domínguez, C.; Paramio, A.; García, A.Z. Do you see it clearly? The effect of packaging and label format on Google Ads. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1648–1666.
  15. King, A.J.; Bol, N.; Cummins, R.G.; John, K.K. Improving visual behavior research in communication science: An overview, review, and reporting recommendations for using eye-tracking methods. Commun. Methods Meas. 2019, 13, 149–177.
  16. Robinson, D.A. A method of measuring eye movement using a scleral search coil in a magnetic field. IEEE. Trans. Biomed. Eng. 1963, 10, 137–145.
  17. Kaufman, A.E.; Bandopadhay, A.; Shaviv, B.D. An Eye Tracking Computer User Interface. In Proceedings of the 1993 IEEE Research Properties in Virtual Reality Symposium, San Jose, CA, USA, 25–26 October 1993; IEEE: Piscataway, NJ, USA, 1993; pp. 120–121.
  18. Babcock, J.S.; Pelz, J.B. Building a Lightweight Eyetracking Headgear. In Proceedings of the 2004 Symposium on Eye Tracking Research & Applications, San Antonio, TX, USA, 22–24 March 2004; Association for Computing Machinery: New York, NY, USA, 2004; pp. 109–114.
  19. Takemura, K.; Takahashi, K.; Takamatsu, J.; Ogasawara, T. Estimating 3-D point-of-regard in a real environment using a head-mounted eye-tracking system. IEEE Trans. Hum. Mach. Syst. 2014, 44, 531–536.
  20. Wade, N.; Tatler, B.W. The Moving Tablet of the Eye: The Origins of Modern Eye Movement Research; Oxford University Press: Oxford, UK, 2005; pp. 14–28.
  21. Valenti, R.; Sebe, N.; Gevers, T. What are you looking at? Improving visual gaze estimation by saliency. Int. J. Comput. Vis. 2012, 98, 324–334.
  22. Lin, Y.T.; Lin, R.Y.; Lin, Y.C.; Lee, G.C. Real-time eye-gaze estimation using a low-resolution webcam. Multimed. Tools Appl. 2013, 65, 543–568.
  23. Cheung, Y.M.; Peng, Q. Eye gaze tracking with a web camera in a desktop environment. IEEE Trans. Hum. Mach. Syst. 2015, 45, 419–430.
  24. Meng, C.; Zhao, X. Webcam-based eye movement analysis using CNN. IEEE Access 2017, 5, 19581–19587.
  25. Meißner, M.; Oll, J. The promise of eye-tracking methodology in organizational research: A taxonomy, review, and future avenues. Organ. Res. Methods 2019, 22, 590–617.
  26. Hansen, D.W.; Pece, A.E. Eye tracking in the wild. Comput. Vis. Image Underst. 2005, 98, 155–181.
  27. Carter, B.T.; Luke, S.G. Best practices in eye tracking research. Int. J. Psychophysiol. 2020, 155, 49–62.
  28. Wedel, M.; Pieters, R. A review of eye-tracking research in marketing. In Review of Marketing Research; Malhotra, N.K., Ed.; Emerald Group Publishing Limited: Bingley, UK, 2008; Volume 4, pp. 123–147.
  29. Bojko, A. Eye Tracking in User Experience Testing: How to Make the Most of It. In Proceedings of the UPA 2005 Conference, Montreal, QC, Canada, 27 June–1 July 2005.
  30. Singh, H.; Singh, J. Human eye tracking and related issues: A review. Int. J. Sci. Res. 2012, 2, 1–9.
  31. Bojko, A. Eye Tracking the User Experience: A Practical Guide to Research; Rosenfeld Media: Brooklyn, NY, USA, 2013; pp. 12–13.
  32. Rayner, K. The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search. Q. J. Exp. Psychol. 2009, 62, 1457–1506.
  33. Duchowski, A.T. Visual attention. In Eye Tracking Methodology, 3rd ed.; Springer: Cham, Switzerland, 2017; pp. 3–13.
  34. Mormann, M.; Griffiths, T.; Janiszewski, C.; Russo, J.E.; Aribarg, A.; Ashby, N.J.S.; Bagchi, R.; Bhatia, S.; Kovacheva, A.; Meissner, M.; et al. Time to pay attention to attention: Using attention-based process traces to better understand consumer decision-making. Mark. Lett. 2020, 31, 381–392.
  35. Allopenna, P.D.; Magnuson, J.S.; Tanenhaus, M.K. Tracking the time course of spoken word recognition using eye movements: Evidence for continuous mapping models. J. Mem. Lang. 1998, 38, 419–439.
  36. Chua, H.F.; Boland, J.E.; Nisbett, R.E. Cultural variation in eye movements during scene perception. Proc. Natl. Acad. Sci. USA 2005, 102, 12629–12633.
  37. Polonio, L.; Di Guida, S.; Coricelli, G. Strategic sophistication and attention in games: An eye-tracking study. Games Econ Behav. 2015, 94, 80–96.
  38. Majaranta, P.; Räihä, K.-J.; Hyrskykari, A.; Špakov, O. Eye movements and human-computer interaction. In Eye Movement Research: An Introduction to Its Scientific Foundations and Applications; Klein, C., Ettinger, U., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 971–1015.
  39. Chapman, P.; Underwood, G.; Roberts, K. Visual search patterns in trained and untrained novice drivers. Transp. Res. F Traffic Psychol. 2002, 5, 157–167.
  40. Nakayasu, H.; Miyoshi, T.; Aoki, H.; Kondo, N.; Patterson, P. Analysis of driver perceptions and behavior when driving in an unfamiliar traffic regulation. J. Adv. Comput. Intell. Intell. Inform. 2011, 15, 1038–1048.
  41. Beach, P.; McConnel, J. Eye tracking methodology for studying teacher learning: A review of the research. Int. J. Res. Method Educ. 2019, 42, 485–501.
  42. Solnais, C.; Andreu-Perez, J.; Sánchez-Fernández, J.; Andréu-Abela, J. The contribution of neuroscience to consumer research: A conceptual framework and empirical review. J. Econ. Psychol. 2013, 36, 68–81.
  43. Hoffman, J.E. Visual attention and eye movements. In Attention; Pashler, H., Ed.; Psychology Press: London, UK, 2016; pp. 119–153.
  44. Li, Z.; Guo, P.; Song, C. A Review of Main Eye Movement Tracking Methods. J. Phys. Conf. Ser. 2021, 1802, 1–13.
  45. Jacob, R.J.; Karn, K.S. Commentary on section 4. Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. In The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research; Hyönä, J., Radach, R., Deubel, H., Eds.; Elsevier: Amsterdam, The Netherlands, 2003; pp. 573–607.
  46. Holmqvist, K.; Andersson, R. Eye tracking: A Comprehensive Guide to Methods, Paradigms and Measures, 2nd ed.; Oxford University Press: Charleston, SC, USA, 2017; pp. 5–7.
  47. Funke, G.; Greenlee, E.; Carter, M.; Dukes, A.; Brown, R.; Menke, L. Which Eye Tracker Is Right for Your Research? Performance Evaluation of Several Cost Variant Eye Trackers. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Washington, DC, USA, 19–23 September 2016; SAGE Publications: Los Angeles, CA, USA, 2016; pp. 1240–1244.
  48. Semmelmann, K.; Weigelt, S. Online webcam-based eye tracking in cognitive science: A first look. Behav. Res. Methods 2018, 50, 451–465.
  49. Dalmaijer, E.S. Is the low-cost EyeTribe eye tracker any good for research? PeerJ Prepr. 2014, 4, 1–35.
  50. Dalmaijer, E.S.; Mathôt, S.; Van der Stigchel, S. PyGaze: An open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments. Behav. Res. Methods 2014, 46, 913–921.
  51. Bulling, A.; Gellersen, H. Toward mobile eye-based human-computer interaction. IEEE Pervasive Comput. 2010, 9, 8–12.
  52. Yüksel, D. Göz izleme tekniği ile bir siyasal pazarlama iletişimi araştırması: Türkiye Cumhuriyeti 2023 Cumhurbaşkanlığı Seçimi billboardlarının analizi. İşletme Araştırmaları Derg. 2023, 15, 2113–2125.
  53. Just, M.A.; Carpenter, P.A. A theory of reading: From eye fixations to comprehension. Psychol. Rev. 1980, 87, 329–354.
  54. Aldayel, M.; Ykhlef, M.; Al-Nafjan, A. Deep learning for EEG based preference classification in neuromarketing. Appl. Sci. 2020, 10, 1525.
  55. Hammou, K.; Galib, M.H.; Melloul, J. The contributions of neuromarketing in marketing research. J. Manag. Res. 2013, 5, 20–23.
  56. Yüksel, D. Marka yönetiminde renk seçimi: Renk kümeleme modeli. Bus. Econ. Res. J. 2023, 14, 433–444.
  57. Hsiao, M.-H. Shopping mode choice: Physical store shopping versus e-shopping. Transp. Res. E Logist. Transp. 2009, 45, 86–95.
  58. Xi, G.; Zhen, F.; Cao, X.; Xu, F. The interaction between e-shopping and store shopping: Empirical evidence from Nanjing, China. Transp. Lett. 2018, 12, 157–165.
  59. Colaço, R.; de Abreu e Silva, J. Exploring the interactions between online shopping, in-store shopping, and weekly travel behavior using a 7-day shopping survey in Lisbon, Portugal. Transp. Res. Rec. 2021, 2675, 379–390.
  60. Spence, C.; Puccinelli, N.M.; Grewal, D.; Roggeveen, A.L. Store atmospherics: A multisensory perspective. Psychol Mark. 2014, 31, 472–488.
  61. Baker, J.; Grewal, D.; Parasuraman, A. The influence of store environment on quality inferences and store image. J. Acad. Mark. Sci. 1994, 22, 328–339.
  62. Manganari, E.E.; Siomkos, G.J.; Vrechopoulos, A.P. Store atmosphere in web retailing. Eur. J. Mark. 2009, 43, 1140–1153.
  63. Demangeot, C.; Broderick, A.J. Conceptualising consumer behaviour in online shopping environments. Int. J. Retail Distrib. Manag. 2017, 35, 878–894.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Business
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Doğuş Yüksel
View Times: 157
Revisions: 2 times (View History)
Update Date: 30 Nov 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback