Inteligencia artificial en los procesos de comercio exterior: estudio piloto sobre preparación, barreras y beneficios esperados en Guayaquil, Ecuador (2026) Artificial intelligence in foreign trade processes: a pilot study of readiness, barriers, and expected benefits in Guayaquil, Ecuador (2026)

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Johanna Cristina Hernandez Mantilla
Chenyin Emilio Wong Muñoz
Gabriel Germán Usiña Báscones

Resumen

El objetivo del estudio fue analizar, en una fase piloto, la preparación para la adopción de inteligencia artificial (IA), las barreras percibidas y los beneficios esperados en los procesos de comercio exterior de empresas ubicadas en Guayaquil, Ecuador. Se desarrolló un estudio aplicado, cuantitativo, no experimental y transversal, con alcance exploratorio-descriptivo para esta fase. El instrumento fue un cuestionario autoadministrado con escala Likert de cinco puntos, organizado en tres dimensiones: preparación para la adopción de IA, barreras para su implementación y beneficios esperados. El piloto registró 15 respuestas. El análisis descriptivo mostró medias de 3.73 para preparación, 3.91 para barreras y 4.16 para beneficios esperados. Los mayores niveles de acuerdo se observaron en la expectativa de reducción del tiempo de preparación, revisión documental y despacho aduanero (86.7 %) y de los costos operativos por embarque (86.7 %). La falta de personal especializado y las preocupaciones sobre privacidad, ciberseguridad y posibles errores de la IA destacaron como barreras, con 80.0 % de acuerdo en ambos casos. Los resultados del piloto sugieren una percepción favorable sobre la utilidad potencial de la IA, pero evidencian la necesidad de fortalecer capacidades humanas, gobernanza de datos, seguridad e interoperabilidad. Debido al tamaño muestral y a la disponibilidad de información agregada por ítem, los hallazgos deben interpretarse como evidencia preliminar; no son generalizables ni permiten establecer causalidad o contrastar de forma definitiva la hipótesis correlacional propuesta

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Hernandez Mantilla, J. C., Wong Muñoz, C. E., & Usiña Báscones G. G. (2026). Inteligencia artificial en los procesos de comercio exterior: estudio piloto sobre preparación, barreras y beneficios esperados en Guayaquil, Ecuador (2026): Artificial intelligence in foreign trade processes: a pilot study of readiness, barriers, and expected benefits in Guayaquil, Ecuador (2026). Revista Científica Multidisciplinar G-Nerando, 7(2), Pág. 4262 –. https://doi.org/10.66473/rcmg.v7i2.1497
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Arvis, J.-F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to compete 2023: Trade logistics in an uncertain global economy - The Logistics Performance Index and its indicators. World Bank. https://doi.org/10.1596/39760

Aslett, J., González, I., Hadwick, D., Hamilton, S., Hardy, M. A., & Pérez, A. (2024). Understanding artificial intelligence in tax and customs administration (Technical Notes and Manuals 2024/006). International Monetary Fund. https://doi.org/10.5089/9798400290435.005

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202. https://doi.org/10.1080/00207543.2018.1530476

Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163, 120447. https://doi.org/10.1016/j.techfore.2020.120447

Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research, 62(9), 3333-3360. https://doi.org/10.1080/00207543.2023.2232050

Cao, Q., & Zheng, X. (2024). Application of artificial intelligence technology in the supervision of customs clearance machine inspection. World Customs Journal, 18(2), 51-76. https://doi.org/10.55596/001c.122754

Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132

Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599

Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management, 59(3), 65-76. https://doi.org/10.1111/jscm.12304

Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846. https://doi.org/10.1080/00207543.2018.1488086

Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120-6145. https://doi.org/10.1080/00207543.2024.2309309

Kosasih, E. E., Papadakis, E., Baryannis, G., & Brintrup, A. (2024). A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches. International Journal of Production Research, 62(4), 1510-1540. https://doi.org/10.1080/00207543.2023.2281663

Li, L., Liu, Y., Jin, Y., Cheng, T. C. E., & Zhang, Q. (2024). Generative AI-enabled supply chain management: The critical role of coordination and dynamism. International Journal of Production Economics, 277, 109388. https://doi.org/10.1016/j.ijpe.2024.109388

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434

Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13-39. https://doi.org/10.1080/13675560902736537

Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain resilience: Learning from COVID-19. The International Journal of Logistics Management, 33(4), 1246-1268. https://doi.org/10.1108/IJLM-02-2021-0094

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250

Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702

Servicio Nacional de Aduana del Ecuador. (2025). Servicios para operadores de comercio exterior. https://www.aduana.gob.ec/servicios-para-oces/

Shahzadi, G., Jia, F., Chen, L., & John, A. (2024). AI adoption in supply chain management: A systematic literature review. Journal of Manufacturing Technology Management, 35(6), 1125-1150. https://doi.org/10.1108/JMTM-09-2023-0431

Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: Mapping the territory. International Journal of Production Research, 60(24), 7527-7550. https://doi.org/10.1080/00207543.2022.2029611

Smyth, C., Dennehy, D., Fosso Wamba, S., Scott, M., & Harfouche, A. (2024). Artificial intelligence and prescriptive analytics for supply chain resilience: A systematic literature review and research agenda. International Journal of Production Research, 62(23), 8537-8561. https://doi.org/10.1080/00207543.2024.2341415

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009

United Nations Conference on Trade and Development. (2024). ASYCUDA report 2024: Innovation for a changing world. United Nations. https://unctad.org/publication/asycuda-report-2024

United Nations Economic Commission for Europe. (2024). White paper on the use of artificial intelligence in trade facilitation. UN/CEFACT. https://unece.org/trade/documents/2024/04/white-paper-use-artificial-intelligence-trade-facilitation

van Hoek, R. (2024). Insight from industry - Early lessons learned about AI adoption in core procurement processes, directions for managers and researchers. Supply Chain Management: An International Journal, 29(4), 794-803. https://doi.org/10.1108/SCM-02-2024-0143

Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019

World Customs Organization. (2025). Detailed report on the adoption of artificial intelligence and machine learning in Customs. WCO Smart Customs Project. https://www.wcoomd.org/en/topics/facilitation/activities-and-programmes/disruptive-technologies/smart-customs-project.aspx

World Trade Organization. (2025). World Trade Report 2025: Making trade and AI work together to the benefit of all. World Trade Organization. https://www.wto.org/english/res_e/publications_e/wtr25_e.htm

Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327(2), 605-632. https://doi.org/10.1007/s10479-022-04983-y