Analysis of customer-system interactions in gastronomic trade processes in the city of Pamplona using an BCI
DOI:
https://doi.org/10.15665/rvgt9394Keywords:
User Experience, Brain–Computer Interface, Conversational Agents, Human–Computer Interaction, Cognitive LoadAbstract
This paper presents a pilot study aimed at selecting an appropriate method for order placement in a fast-food restaurant located in the city of Pamplona. An experimental design was implemented to compare process performance indicators using three interaction methods as independent variables: telephone calls, communication with a human operator through an instant messaging system, and a conversational interface or chatbot generated using artificial intelligence tools. The experimental evaluation employed techniques based on the recording of neurophysiological signals using a brain–computer interface to estimate emotional states such as attention, together with traditional approaches for deriving operational performance metrics and assessing customer perception. An algorithm was proposed to jointly analyze all performance indicators, and the results showed that the chatbot-based interaction method achieved the highest overall score.
References
E. M. Ghazali, D. S. Mutum, y J. J. Cheah, “Dining with robots: An integrated perspective on functional, emotional and relational dimensions of customer experience”, Electronic Markets, vol. 35, núm. 1, dic. 2025, doi: 10.1007/s12525-025-00797-5.
E. ; H. N. Yıldız, “Why do customers intend to dine at robot-chef restaurants? The roles of entertainment, consistency, authenticity, and food quality”, Int. J. Gastron. Food Sci., vol. 42, núm. December 2025, oct. 2025.
M. O. Parvez, “The Algorithmic Appetite: Reflections on the Future of Robotic Chefs in the Restaurant Industry”, Journal of Culinary Science & Technology, vol. 23, núm. 3, pp. 349–353, may 2025, doi: 10.1080/15428052.2025.2495646.
M. Amin, M. O. Parvez, S. Rasool, L. Aureliano-Silva, y A. Dang, “Human–robot interaction attributes at the restaurants: will it enhance revisit intentions?”, Journal of Hospitality and Tourism Technology, vol. 16, núm. 5, pp. 1024–1045, may 2025, doi: 10.1108/JHTT-10-2024-0694.
E. Popescu, L. Laura, F. Barbosa Escobar, y R. Niewiadomski, “Social robots as eating companions”, Front. Comput. Sci., pp. 1–13, ago. 2022, [En línea]. Disponible en: 10.3389/fcomp.2022.909844
M. F. Shorbaji, A. A. Alalwan, y R. Algharabat, “AI-Enabled Mobile Food-Ordering Apps and Customer Experience: A Systematic Review and Future Research Agenda”, el 1 de septiembre de 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/jtaer20030156.
B. Ren, Q. Zhou, y J. Chen, “Assessing cognitive workloads of assembly workers during multi-task switching”, Sci. Rep., vol. 13, núm. 1, dic. 2023, doi: 10.1038/s41598-023-43477-0.
J. Wang, A. H. Alsharif, N. Abd Aziz, A. Khraiwish, y N. Z. M. Salleh, “Neuro-Insights in Marketing Research: A PRISMA-Based Analysis of EEG Studies on Consumer Behavior”, el 1 de octubre de 2024, SAGE Publications Inc. doi: 10.1177/21582440241305365.
T. Jiang, J. Wu, y S. C. H. Leung, “The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis”, Front. Comput. Neurosci., vol. 19, 2025, doi: 10.3389/fncom.2025.1556483.
X. Y. Leung y H. Wen, “Chatbot usage in restaurant takeout orders: A comparison study of three ordering methods”, Journal of Hospitality and Tourism Management, vol. 45, pp. 377–386, dic. 2020, doi: 10.1016/j.jhtm.2020.09.004.
G. S. Urumutta Hewage, L. Boman, y S. Lefebvre, “Deciphering the dilemma: The surprising impact of QR code menus on diminishing customer loyalty”, Journal of Hospitality and Tourism Management, vol. 61, pp. 187–190, dic. 2024, doi: 10.1016/j.jhtm.2024.10.006.
M. Gupta, V. Dheekonda, y M. Masum, “Genie: Enhancing information management in the restaurant industry through AI-powered chatbot”, International Journal of Information Management Data Insights, vol. 4, núm. 2, nov. 2024, doi: 10.1016/j.jjimei.2024.100255.
M. Romero-Charneco, A. M. Casado-Molina, P. Alarcón-Urbistondo, y J. P. Cabrera Sánchez, “Customer intentions toward the adoption of WhatsApp chatbots for restaurant recommendations”, Journal of Hospitality and Tourism Technology, vol. 16, núm. 4, pp. 784–816, jun. 2025, doi: 10.1108/JHTT-01-2024-0024.
L. Moreno, C. Peña, y H. González, “Integración de un sistema de neuroseñales para detectar expresiones en el análisis de material multimedia”, Revista Facultuda de Ingeniería, vol. 24, núm. 38, pp. 29–40, 2014, [En línea]. Disponible en: http://revistas.uptc.edu.co/index.php/ingenieria/article/view/3156/4343
C. Paez, C. Peña, y A. Pardo, “Estudio de la influencia de la actividad física sobre la calidad del sueño y los niveles de atención utilizando herramientas tecnológicas”, Mundo FESC, vol. 11, núm. 4s, pp. 310–323, 2021, [En línea]. Disponible en: https://doi.org/10.61799/2216-0388.1110
C. Peña, S. Caicedo, L. Moreno, M. Maestre, y A. Pardo, “Use of a Low Cost Neurosignals Capture System to Show the Importance of Developing Didactic Activities Within a Class to Increase the Level of Student Engagement. ( Case Study )”, WSEAS Transaction on Computers, vol. 16, pp. 172–178, 2017, [En línea]. Disponible en: http://www.wseas.org/multimedia/journals/computers/2017/a385905-070.php
L. Moreno, C. Peña, M. Maestre, S. Caicedo, y A. Pardo, “Registro de Neuroseñales con una Interfaz Cerebro-Computador para Estimar el Nivel Estrés en un Estudiante durante una Clase”, INGE-CUC, vol. 13, núm. 2, pp. 95–101, 2017, doi: http://doi.org/10.17981/ingecuc.13.2.2017.10.
M. Pušica et al., “Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study”, Brain Sci., vol. 14, núm. 2, feb. 2024, doi: 10.3390/brainsci14020149.
H. Meng, X. Lu, y J. Xu, “The Impact of Chatbot Response Strategies and Emojis Usage on Customers’ Purchase Intention: The Mediating Roles of Psychological Distance and Performance Expectancy”, Behavioral Sciences, vol. 15, núm. 2, feb. 2025, doi: 10.3390/bs15020117.
A. Kovari, “Explainable AI chatbots towards XAI ChatGPT: A review”, Heliyon, vol. 11, núm. 2, ene. 2025, doi: 10.1016/j.heliyon.2025.e42077.
N. Alharbi, F. Ud Din, D. Paul, y E. Sadgrove, “Driving AI chatbot adoption: A systematic review of factors, barriers, and future research directions”, Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, núm. 3, sep. 2025, doi: 10.1016/j.joitmc.2025.100590.
P. Fosci y G. Psaila, “Towards flexible retrieval, integration and analysis of json data sets through fuzzy sets: A case study”, Information (Switzerland), vol. 12, núm. 7, jul. 2021, doi: 10.3390/info12070258.
S. You, H. W. Ji, H. Kwak, T. Chung, y M. Bae, “Schema-Agnostic Data Type Inference and Validation for Exchanging JSON-Encoded Construction Engineering Information”, Buildings, vol. 15, núm. 17, sep. 2025, doi: 10.3390/buildings15173159.
S. Derdiyok, F. P. Akbulut, y C. Catal, “Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset”, Data Brief, vol. 52, feb. 2024, doi: 10.1016/j.dib.2023.109896.
G. Niso, E. Romero, J. T. Moreau, A. Araujo, y L. R. Krol, “Wireless EEG: A survey of systems and studies”, Neuroimage, vol. 269, abr. 2023, doi: 10.1016/j.neuroimage.2022.119774.
M. S. Salim, S. I. Hossain, T. Jalal, D. K. Bose, y M. J. I. Basher, “LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering”, SoftwareX, vol. 29, feb. 2025, doi: 10.1016/j.softx.2024.102029.
M. Galigani, N. Castellani, B. Italia, S. D’Aversa, D. Bottari, y F. Garbarini, “Frequency-tagging EEG reveals spontaneous categorical discrimination of visual self-identity”, iScience, vol. 28, núm. 10, oct. 2025, doi: 10.1016/j.isci.2025.113562.
C. Rico-Olarte, B. M. Eskofier, y D. M. Lopez, “EEG-cleanse: an automated pipeline for cleaning electroencephalography recordings during full-body movement”, MethodsX, vol. 15, dic. 2025, doi: 10.1016/j.mex.2025.103702.
J. Sabio, N. S. Williams, G. M. McArthur, y N. A. Badcock, “A scoping review on the use of consumer-grade EEG devices for research”, PLoS One, vol. 19, núm. 3 March, mar. 2024, doi: 10.1371/journal.pone.0291186.
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