La importancia de los sistemas cognitivos en el proceso de toma de decisiones de los viajeros: Aplicación de la teoría de procesos duales en el análisis de las reseñas online de hoteles
DOI:
https://doi.org/10.25145/j.pasos.2026.24.021Palabras clave:
Reseñas en línea, Teoría de Procesos Duales, Sistemas Cognitivos, Hoteles, Aprendizaje AutomáticoResumen
Basado en la Teoría de Procesos Duales (TPD), este artículo investigó la importancia del procesamiento de los atributos de las reseñas en línea por los sistemas cognitivos en el proceso de toma de decisiones de los viajeros. Se utilizaron redes neuronales tipo Transformer para un análisis textual multidimensional de 89,290 reseñas en línea (RE). La investigación cuantitativa probó las hipótesis mediante tres algoritmos de aprendizaje automático: árboles de decisión, random forest y XGBoost, utilizando la técnica de importancia de características. Los resultados indicaron que los sistemas 1 (procesamiento heurístico, rápido e intuitivo) y 2 (procesamiento sistemático, lento y racional) operan de manera simultánea y complementaria en el proceso de toma de decisiones y muestran un efecto aditivo. El sistema 2 desempeña un papel significativo en la toma de decisiones de los viajeros, esto es, los consumidores examinan cuidadosamente el contenido de las RE antes de reservar un hotel. La investigación enriquece la comprensión de la aplicación de la TPD en los procesos de toma de decisiones en línea y amplía su alcance al proponer el análisis multidimensional del contenido de plataformas digitales. El estudio también contribuye a la gestión hotelera al identificar la importancia de cada dimensión de las RE.
Descargas
Datos de publicación
Perfil evaluadores/as N/D
Declaraciones de autoría
- Editor y equipo editorial
- Perfiles
- Sociedad académica
- PASOS. Revista de Turismo y Patrimonio Cultural
- Editorial
- Instituto Universitario de Investigación Social y Turismo. Universidad de La Laguna (España) - Instituto Universitario da Maia ISMAI (Portugal)
Para obtener más información sobre un dato, haga clic 
PF es mantenido por el Public Knowledge Project
Citas
Abdel-Maksoud, A., Kamel, H., & Elbanna, S. (2016). Investigating relationships between stakeholders’ pressure, eco-control systems and hotel performance. International Journal of Hospitality Management, 59, 95-104. https://doi.org/10.1016/j.ijhm.2016.09.006
Abedi, R., Costache, R., Shafizadeh-Moghadam, H., & Pham, Q. B. (2022). Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International, 37(19), 5479-5496. https://doi.org/10.1080/10106049.2021.1920636
Aureliano-Silva, L., Leung, X., & Spers, E. E. (2021). The effect of online reviews on restaurant visit intentions: Applying signaling and involvement theories. Journal of Hospitality and Tourism Technology, 12(4), 672-688. https://doi.org/10.1108/JHTT-06-2020-0143
Boehmke B., & Greenwell B.M. (2019). Hands-on machine learning with R. Boca Raton (FL): CRC Press.
Bortoluzzi, D. A., Lunkes, R. J., Santos, E. A. d., & Mendes, A. C. A. (2020). Effect of online hotel reviews on the relationship between defender and prospector strategies and management controls. International Journal of Contemporary Hospitality Management, 32(12), 3721-3745. https://doi.org/10.1108/IJCHM-04-2020-0297
Breiman, L. (2001). Random forest, 45. Mach Learn, 1.
Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1984). Classification and regression trees. Belmont (CA): Wadsworth International Group, 432, 151–166.
Casalicchio, G., Molnar, C., & Bischl, B. (2019). Visualizing the feature importance for black box models. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I 18 (pp. 655-670). Springer International Publishing.
Chang, K. C. (2013). How reputation creates loyalty in the restaurant sector. International Journal of Contemporary Hospitality Management, 25(4), 536-557. https://doi.org/10.1108/09596111311322916
Chang, H. H., Fang, P. W., & Huang, C. H. (2016). The impact of on-line consumer reviews on value perception: the dual-process theory and uncertainty reduction. In Web design and development: Concepts, methodologies, tools, and applications (pp. 1498-1524). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-4666-8619-9.ch068
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., Ghosh, S. K., & Chaudhuri, S. (2021). Social customer relationship management factors and business benefits. International Journal of Organizational Analysis, 29(1), 35-58. https://doi.org/10.1108/IJOA-11-2019-1933
Eslami, S. P., Ghasemaghaei, M., & Hassanein, K. (2018). Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113, 32-42. https://doi.org/10.1016/j.dss.2018.06.012
Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of business research, 68(6), 1261-1270. https://doi.org/10.1016/j.jbusres.2014.11.006
Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956-970. https://doi.org/10.1016/j.im.2018.04.010
Ghasemaghaei, M., Eslami, S.P., Deal, K. and Hassanein, K. (2018), "Reviews’ length and sentiment as correlates of online reviews’ ratings", Internet Research, Vol. 28 No. 3, pp. 544-563. https://doi.org/10.1108/IntR-12-2016-0394
Ghosh, T. (2017). Managing negative reviews: the persuasive role of webcare characteristics. Journal of Internet Commerce, 16(2), 148-173. https://doi.org/10.1080/15332861.2017.1305254
Grayot, J. (2019). From selves to systems: on the intrapersonal and intraneural dynamics of decision making. Journal of Economic Methodology, 26(3), 208-227. https://doi.org/10.1080/1350178X.2019.1625213
Grayot, J. D. (2020). Dual process theories in behavioral economics and neuroeconomics: A critical review. Review of Philosophy and Psychology, 11 (1), 105–136. https://doi.org/10.1007/s13164-019-00446-9
Herjanto, H., Amin, M., & Cobanoglu, C. (2025). Should I use ChatGPT travel insurance recommendations? A dual-process theory perspective. International Journal of Consumer Studies. https://doi.org/10.1111/ijcs.70044
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?. Journal of Interactive Marketing, 18(1), 38-52. https://doi.org/10.1002/dir.10073
Huang, D., So, K. K. F., Huang, J., & Huang, S. (2025). Exploring the attractiveness of digital human influencers in destination marketing: The allure of two-path meaning transfer. Tourism Management, 110, 105166. https://doi.org/10.1016/j.tourman.2025.105166
Jang, S., Chung, J., & Rao, V. R. (2021). The importance of functional and emotional content in online consumer reviews for product sales: Evidence from the mobile gaming market. Journal of Business Research, 130, 583-593. https://doi.org/10.1016/j.jbusres.2019.09.027
Jeesha, K., & Purani, K. (2021). Webcare as a signal: exhaustive-selective webcare strategy and brand evaluation. European Journal of Marketing, 55(7), 1930-1953. https://doi.org/10.1108/EJM-05-2019-0421
Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In Kahneman, D., & Gilovich, T. (Eds.), Heuristics and biases: The Psychology of Intuitive Judgment, 49–81.
Kahneman, D., & Frederick, S. (2005). A model of heuristic judgment. In Holyoak, K., &Morrison, R. (Eds.), The Cambridge handbook of thinking and reasoning, (pp. 267–293). Cambridge University Press.
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Judgement under uncertainty: Heuristics and biases, (pp. 3–20). Cambridge University Press.
Kitsios, F., Kamariotou, M., Karanikolas, P., & Grigoroudis, E. (2021). Digital marketing platforms and customer satisfaction: Identifying eWOM using big data and text mining. Applied Sciences, 11(17), 8032. https://doi.org/10.3390/app11178032
Kwon, W., Lee, M., Back, K. J., & Lee, K. Y. (2021). Assessing restaurant review helpfulness through big data: dual-process and social influence theory. Journal of Hospitality and Tourism Technology, 12(2), 177-195. https://doi.org/10.1108/JHTT-04-2020-0077
Lawrie, E., Flus, M., Olechowski, A., Hay, L., & Wodehouse, A. (2024). From theory to practice: a roadmap for applying dual-process theory in design cognition research. Journal of Engineering Design, 1-21. https://doi.org/10.1080/09544828.2024.2336837
Liu, Y. and Hu, H.-f. (2021), "Online review helpfulness: the moderating effects of review comprehensiveness", International Journal of Contemporary Hospitality Management, Vol. 33 No. 2, pp. 534-556. https://doi.org/10.1108/IJCHM-08-2020-0856
Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140-151. https://doi.org/10.1016/j.tourman.2014.09.020
Lunkes, R. J., Bortoluzzi, D. A., Anzilago, M., & da Rosa, F. S. (2020). Influence of online hotel reviews on the fit between strategy and use of management control systems: A study among small-and medium-sized hotels in Brazil. Journal of Applied Accounting Research, 21(4), 615-634. https://doi.org/10.1108/JAAR-06-2018-0090
Lunkes, R. J., Deggau, L., Codesso, M.M., Rosa, F. S., & Monteiro, J.J. (2025). The Influence of Online Reviews and Hotel Digital Responsibility on ESG Practices and Sustainability Performance. International Journal of Contemporary Hospitality Management, ahead-of-print, ahead-of-print.
Mandolfo, M., Bettiga, D., Lamberti, L., & Noci, G. (2022). Influence of sales promotion on impulse buying: A dual process approach. Journal of Promotion Management, 28(8), 1212-1234. https://doi.org/10.1080/10496491.2022.2060415
Meek, S., Wilk, V., & Lambert, C. (2021). A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews. Journal of Business Research, 125, 354-367. https://doi.org/10.1016/j.jbusres.2020.12.001
Mohaghegh, M., & Größler, A. (2020). The dynamics of operational problem-solving: A dual-process approach. Systemic Practice and Action Research, 33(1), 27-54. https://doi.org/10.1007/s11213-019-09513-9
Nicolau, J.L., Xiang, Z. and Wang, D. (2024), "Daily online review sentiment and hotel performance", International Journal of Contemporary Hospitality Management, Vol. 36 No. 3, pp. 790-811. https://doi.org/10.1108/IJCHM-05-2022-0594
Paget, S. (2024). 2024 local consumer review survey. Brightlocal. Acesso em 28 de Março de 2024, de https://www.brightlocal.com/research/local-consumer-review-survey/.
Parikh, A. A., Behnke, C., Almanza, B., Nelson, D., & Vorvoreanu, M. (2017). Comparative content analysis of professional, semi-professional, and user-generated restaurant reviews. Journal of Foodservice Business Research, 20(5), 497-511. https://doi.org/10.1080/15378020.2016.1219170
Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers’ hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605–611. https://doi.org/10.1080/13683500.2018.1549025
Rosillo-Díaz, E., Muñoz-Rosas, J. F., & Blanco-Encomienda, F. J. (2024). Impact of heuristic–systematic cues on the purchase intention of the electronic commerce consumer through the perception of product quality. Journal of Retailing and Consumer Services, 81, 103980. https://doi.org/10.1016/j.jretconser.2024.103980
Roy, G. (2023). Travelers’ online review on hotel performance–Analyzing facts with the Theory of Lodging and sentiment analysis. International Journal of Hospitality Management, 111, 103459. https://doi.org/10.1016/j.ijhm.2023.103459
Saarela, M., & Jauhiainen, S. (2021). Comparison of feature importance measures as explanations for classification models. SN Applied Sciences, 3(2), 272. https://doi.org/10.1007/s42452-021-04148-9
Shin, H. W., Fan, A., & Wu, L. (2022). Trust the Facts: The Impact of Reviews’ Written Style and Subject-Focus on Peer-to-Peer Accommodation Consumption. Journal of Hospitality & Tourism Research, 48(2), 249-276. https://doi.org/10.1177/10963480221100244 (Original work published 2024)
Srivastava, V., & Kalro, A. D. (2019). Enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. Journal of Interactive Marketing, 48(1), 33-50. https://doi.org/10.1016/j.intmar.2018.12.003
Standing, C., Holzweber, M., & Mattsson, J. (2016). Exploring emotional expressions in e-word-of-mouth from online communities. Information Processing & Management, 52(5), 721-732. https://doi.org/10.1016/j.ipm.2016.01.001
Steur, A. J., Fritzsche, F., & Seiter, M. (2022). It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms. Electronic Markets, 1-34. https://doi.org/10.1007/s12525-022-00525-3
TripAdvisor (2024). https://tripadvisor.mediaroom.com/US-about-us Acesso em 05 de março de 2024.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.
van Noort, G., & Willemsen, L. M. (2012). Online damage control: The effects of proactive versus reactive webcare interventions in consumer-generated and brand-generated platforms. Journal of Interactive Marketing, 26(3), 131-140. https://doi.org/10.1016/j.intmar.2011.07.001
Wang, Y., Tariq, S., & Alvi, T. H. (2021). How primary and supplementary reviews affect consumer decision making? Roles of psychological and managerial mechanisms. Electronic Commerce Research and Applications, 46, 101032. https://doi.org/10.1016/j.elerap.2021.101032
Wang, Q., Zhang, W., Li, J., Mai, F., & Ma, Z. (2022). Effect of online review sentiment on product sales: The moderating role of review credibility perception. Computers in Human Behavior, 133, 107272. https://doi.org/10.1016/j.chb.2022.107272
Wason, P. C., & Evans, J. S. B. (1974). Dual processes in reasoning?. Cognition, 3(2), 141-154. https://doi.org/10.1016/0010-0277(74)90017-1
Yan, L., & Wang, X. (2018). Why posters contribute different content in their positive online reviews: A social information-processing perspective. Computers in Human Behavior, 82, 199-216. https://doi.org/10.1016/j.chb.2018.01.009
Zhai, M., Wang, X., & Zhao, X. (2024). The importance of online customer reviews characteristics on remanufactured product sales: Evidence from the mobile phone market on Amazon. com. Journal of Retailing and Consumer Services, 77, 103677. https://doi.org/10.1016/j.jretconser.2023.103677
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Gabriel UFSC, Rogério João Lunkes, Fabricia Silva da Rosa
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Confirmo que el trabajo es original (de mi/nuestra autoría), y que no se someterá a otras revistas o publicaciones hasta la resolución definitiva del proceso de revisión en PASOS, RTPC.
Autorizo la publicación de mi trabajo por PASOS, RTPC de acceso abierto y gratuito en cualquiera de los formatos que estime oportunos, por un plazo indeterminado y a título de colaboración no remunerada.
Asimismo, el/los autor/es entiende/n que el trabajo publicado podrá vincularse o depositarse en cualquier servidor o ser incluido en otras publicaciones (republicación), siempre y cuando el nuevo lugar y/o la nueva edición referencie la publicación original y reconozca la autoría y la propiedad del copyright de las publicaciones de PASOS RTPC.
Los/as autores/as entienden que se realizará una comprobación de plagio-autoplagio, pudiendo retirarse el artículo en cualquier momento del flujo editorial
