Business Neuromanagement: Brain Technology and Work Performance Optimization

Authors

Keywords:

consumer neuroscience, attention, emotion, advertising effectiveness, audiovisual formats

Abstract

Introduction: It was analyzed how attention and neurophysiological activation explain advertising effectiveness in digital environments. The approach is based on the convergence between neuroscience and marketing (EEG, fMRI, eye-tracking and galvanic response), given the limitations of self-reporting to capture rapid and unconscious processes of attention and emotion. Hypotheses were raised about the positive relationship between attention/emotion and memory, intention and attitude, as well as differences by format (video vs. image).
Method: Quantitative, non-experimental, cross-sectional and correlational study. Convenience sample of 120 consumers (18–45 years old). Portable EEG was recorded for attention and emotion indices; then, a Likert questionnaire (12 items) of recall, intention and attitude. Procedure: individual exposure to 6 advertisements (3 videos, 3 images) of 30 s with pauses. Analysis in SPSS v28: descriptive, Pearson and ANOVA (p < .05).
Results: Videos outperformed images in attention, emotion, recall, intention, and attitude (gaps ~1.1–1.6 points). Positive correlations: attention, ↔ recall/attitude; emotion ↔ intention. Scatter plots showed ascending linear trends; ANOVA indicated significant differences by format.
Conclusions: Applied neuroscience enhances the prediction of advertising effectiveness and guides the design of more effective audiovisual messages. Its use requires neuroethics, transparency and protection of mental privacy. It is recommended to expand samples, compare cultural contexts, and explore longitudinal designs to strengthen generalization and the balance between innovation and cognitive autonomy.

References

Bublitz, C. (2024). Neurotechnologies and human rights: Restating and reaffirming the multi-layered protection of the person. The International Journal of Human Rights, 28(5), 782–807. https://doi.org/10.1080/13642987.2024.2310830

Brown, C. M. L. (2024). Neurorights, mental privacy, and mind reading. Neuroethics, 17, 34. https://doi.org/10.1007/s12152-024-09568-z

Cortina, A., & Conill, J. (2019). Bioética y neuroética. Arbor, 195(792), a503. https://doi.org/10.3989/arbor.2019.792n2004

European Parliamentary Research Service. (2024). The protection of mental privacy in the area of neuroscience: Societal, legal and ethical challenges (PE 757.807). Panel for the Future of Science and Technology (STOA), European Parliament. https://doi.org/10.2861/869928

Fang, X., Li, H., Ma, J., Xing, X., Fu, Z., Antwi-Afari, M. F., & Umer, W. (2023). Assessment of construction workers’ spontaneous mental fatigue based on non-invasive and multimodal in-ear EEG sensors. Buildings, 13(2), 321. https://doi.org/10.3390/buildings13020321

Farah, M. J. (2015). An ethics toolbox for neurotechnology. Neuron, 86(1), 34–37. https://doi.org/10.1016/j.neuron.2015.03.038

Giron, A. (2013). Aportaciones a la teoría del desarrollo desde la perspectiva latinoamericana. Problemas del Desarrollo, 44(173), 3–7. https://doi.org/10.1016/S0301-7036(13)71872-8

Ienca, M. (2021). On neurorights. Frontiers in Human Neuroscience, 15, 701258. https://doi.org/10.3389/fnhum.2021.701258

Iqbal, M. U., Srinivasan, B., & Srinivasan, R. (2020). Dynamic assessment of control room operator’s cognitive workload using electroencephalography (EEG). Computers & Chemical Engineering, 141, 106726. https://doi.org/10.1016/j.compchemeng.2020.106726

Juárez-Varón, D., Zuluaga, J. C. S., & Recuerda, A. M. (2024). Neuroentrepreneurship: State of the art and future lines of work. International Entrepreneurship and Management Journal, 20, 2939–2953. https://doi.org/10.1007/s11365-024-00969-3

Madurga Revilla, P., López Pisón, J., Samper Villagrasa, P., García Íñiguez, J. P., Garcés Gómez, R., Domínguez Cajal, M., & Gil Hernández, I. (2020). Patología neurológica en una unidad de cuidados intensivos pediátricos de tercer nivel. Evolución funcional. Nuestra experiencia. Neurología, 35(6), 381–394. https://doi.org/10.1016/j.nrl.2017.09.007

Midha, S., Maior, H. A., Wilson, M. L., & Sharples, S. (2021). Measuring mental workload variations in office work tasks using fNIRS. International Journal of Human-Computer Studies, 147, 102580. https://doi.org/10.1016/j.ijhcs.2020.102580

Mijović, B., Pušica, M., Kartali, A., Bojović, L., Gligorijević, I., Jovanović, J., Leva, M. C., & Mijović, B. (2023). Mental workload classification and tasks detection in multitasking: Deep learning insights from EEG study. Brain Sciences, 13(9), 1281. https://doi.org/10.3390/brainsci13091281

Muhl, E. (2024). The challenge of wearable neurodevices for workplace monitoring: An EU legal perspective. Frontiers in Human Dynamics, 6, 1473893. https://doi.org/10.3389/fhumd.2024.1473893

Muhl, E., & Andorno, R. (2023). Neurosurveillance in the workplace: Do employers have the right to monitor employees’ minds? Frontiers in Human Dynamics, 5, 1245619. https://doi.org/10.3389/fhumd.2023.1245619

Muñoz, J. M., & Marinaro, J. Á. (2023). Neurorights as reconceptualized human rights. Frontiers in Political Science, 5, 1322922. https://doi.org/10.3389/fpos.2023.1322922

Ogoh, G., Akintoye, S., Eke, D., Farisco, M., Fernow, J., Grasenick, K., Guerrero, M., Rosemann, A., Salles, A., & Ulucane, I. (2023). Desarrollar capacidades para la investigación e innovación responsables (IIR). Revista de Tecnología Responsable, 15, 100065. https://doi.org/10.1016/j.jrt.2023.100065

Pérez, C., & Vásquez, C. (2012). Contribución de la neuropsicología al diagnóstico de enfermedades neuropsiquiátricas. Revista Médica Clínica Las Condes, 23(5), 530–541. https://doi.org/10.1016/S0716-8640(12)70347-4

Robinson, J. T., Rommelfanger, K. S., Anikeeva, P. O., Etienne, A., French, J., Gelinas, J., Grover, P., & Picard, R. (2022). Building a culture of responsible neurotech: Neuroethics as socio-technical challenges. Neuron, 110(13), 2057–2062. https://doi.org/10.1016/j.neuron.2022.05.005

Sang-Ho, Y., Choi, K., Nam, S., Yoon, E.-K., Sohn, J.-W., & Oh, B.-M. (2023). Development of Korea Neuroethics Guidelines. Journal of Korean Medical Science, 38(25), e193. https://doi.org/10.3346/jkms.2023.38.e193

Steindl, E. (2024). Consumer neuro devices within EU product safety law: Are we prepared for big tech ante portas? Computer Law & Security Review, 52, 105945. https://doi.org/10.1016/j.clsr.2024.105945

Wascher, E., Reiser, J., Rinkenauer, G., Larrá, M., Dreger, F. A., Schneider, D., Karthaus, M., Getzmann, S., Gutberlet, M., & Arnau, S. (2021). Neuroergonomics on the go: An evaluation of the potential of mobile EEG for workplace assessment and design. Human Factors: The Journal of the Human Factors and Ergonomics Society, 65(1), 86–106. https://doi.org/10.1177/00187208211007707

Wiediartini, C., Ciptomulyono, U., & Dewi, R. S. (2023). Evaluation of physiological responses to mental workload in n-back and arithmetic tasks. Ergonomics, 67(8), 1121–1133. https://doi.org/10.1080/00140139.2023.2284677

Yoo, S.-H., Choi, K., Nam, S., Yoon, E.-K., Sohn, J.-W., & Oh, B.-M. (2023). Development of Korea Neuroethics Guidelines. Journal of Korean Medical Science, 38(25), e193. https://doi.org/10.3346/jkms.2023.38.e193

Zhang, Y., Jia, M., Chen, T., Li, M., Wang, J., Hu, X., & Xu, Z. (2024). A neuroergonomics model for evaluating nuclear power plants operators’ performance under heat stress driven by ECG time-frequency spectrums and fNIRS prefrontal cortex network: A CNN-GAT fusion model. Advanced Engineering Informatics, 62(A), 102563. https://doi.org/10.1016/j.aei.2024.102563

Downloads

Published

2024-12-30

Issue

Section

Original

How to Cite

1.
Zaragoza Alvarado GA. Business Neuromanagement: Brain Technology and Work Performance Optimization. NeuroData [Internet]. 2024 Dec. 30 [cited 2026 Mar. 21];1:52. Available from: https://neuro.jogbeditorial.ec/index.php/neuro/article/view/52