Innovative digital product management based on a cognitive approach
Innovation management in the modern digital environment requires a shift from intuitive decisions to objective neurophysiological data that can predict the effectiveness of digital product promotion online. This article presents the results of an experiment using functional near-infrared spectroscopy (fNIRS), which measures changes in blood flow to active areas of the brain and evaluates unconscious neural responses to static and dynamic digital products (e.g., website pages, online videos, etc.). The goal of the experiment was to develop recommendations for creating digital products (video content) with predictably high engagement and memorability rates based on objective biometric data. The results of the study demonstrated that a cognitive approach combined with fNIRS creates the basis for innovative digital product management from content development to product launch. The resulting neuromarkers not only allow for the identification of weaknesses in existing video materials but also the prediction of the effectiveness of new formats before their publication, significantly reducing investment risks and increasing conversion rates.
Kleshchunova, A. O., Pryadko, S. N. (2026), “Innovative digital product management based on a cognitive approach”, Research Result. Business and Service Technologies, 12 (3), pp. 63-74. DOI: 10.18413/2408-9346-2026-12-3-0-5
















While nobody left any comments to this publication.
You can be first.
Balardin, J. B. et al. (2017), “Imaging brain function with functional near-infrared spectroscopy in unconstrained environments”, Frontiers in human neuroscience, Т. 11, pp. 258.
Balconi, M. and Molteni, E. (2016), “Past and future of near-infrared spectroscopy in studies of emotion and social neuroscience”, Journal of Cognitive Psychology, Т. 28, 2, pp. 129-146.
Berka, C. et al. (2007), “EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks”, Aviation, space, and environmental medicine, Т. 78, 5, pp. B231-B244.
Chu, M. et al. “Advances in Functional Near‐Infrared Spectroscopy: Physical Principles and Expanding Applications in Neuroscience”, Journal of Biophotonics, Т. 18, 11, pp. e202500147.
Krampe, C. (2022), “The application of mobile functional near-infrared spectroscopy for marketing research–a guideline”, European Journal of Marketing, Т. 56,13, pp. 236-260.
Paulmurugan, K. et al. (2021), “Brain–computer interfacing using functional near-infrared spectroscopy (fNIRS)”, Biosensors, Т. 11, 10, pp. 389.
Pascual-Roa, B. et al. (2026), “EEG biomarkers of cognitive load: Insights from incremental element encoding in short-term working memory”, Biomedical Signal Processing and Control, Т. 112, pp. 108511.
Pinti, P. et al. (2018), “A review on the use of wearable functional near‐infrared spectroscopy in naturalistic environments”, Japanese Psychological Research, Т. 60, 4, pp. 347-373.
Pinti, P. et al. (2020), “The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience”, Annals of the New York Academy of Sciences, Т. 1464, 1, pp. 5-29.
Pryadko, S. N. (2025), Neuromarketing Research for Science and Business, Belgorod, BelSU ID NRU BelSU, 106 p. (In Russ.).
Pryadko, S. N. (2026), Marketing of Innovations: Theoretical Aspects and Applied Solutions, Belgorod, CPP NRU "BelSU", 78 p. (In Russ.).
Xie, H. et al. (2024), “Evaluation of the learning state of online video courses based on functional near infrared spectroscopy”, Biomedical Optics Express, Т. 15, 3, pp. 1486-1499.
Yu, D. et al. (2023), “Research on user experience of the video game difficulty based on flow theory and fNIRS”, Behaviour & Information Technology, Т. 42, 6, pp. 789-805.