Del confinamiento al hábito: intención continuada en aplicaciones de delivery en Ecuador
From lockdown to habit: Continuance intention in food delivery apps in Ecuador
José Luis Castillo Burbano
,
Marlon Manya Orellana
,
Segundo F. Vilema-Escudero
,
María de los Ángeles Solís Tazán
,
Jossep Rafael Cevallos Arias
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Suma de Negocios, 17(37), 135-144, julio-diciembre 2026, ISSN 2215-910X
https://doi.org/10.14349/sumneg/2026.V17.N37.A3
Recibido: 25 de marzo de 2026
Aceptado: 12 de julio de 2026
Online: 26 de agosto de 2026
Introducción/objetivo: la pandemia de Covid-19 transformó los patrones de consumo alimentario a nivel global. Este estudio analiza los cambios actitudinales y conductuales reportados por consumidores de las dos principales ciudades del Ecuador hacia las aplicaciones de delivery de alimentos (FDA) en tres períodos: prepandemia (2019), pandemia (2020-2021) y pospandemia (2022-2025).
Metodología: estudio transversal con enfoque retrospectivo y diseño no experimental. Se aplicó un cuestionario basado en el modelo UTAUT2 extendido con confianza y riesgo percibidos por Covid-19 a una muestra de conveniencia de 468 consumidores. Los datos se analizaron mediante PLS-SEM y análisis multigrupo (MGA).
Resultados: el modelo explica el 67.3 % de la varianza en la intención de uso continuado. Los predictores significativos más fuertes fueron el hábito (β = .52), la expectativa de desempeño (β = .41) y la confianza percibida (β = .29). Quito mostró mayor peso del hábito
(β = .58 vs. .47) y Guayaquil de la influencia social (β = .46 vs. .31).
Conclusiones: los participantes reportaron un nivel de uso que se mantiene por encima del período prepandémico. El estudio aporta la primera evidencia empírica sobre los determinantes de la intención continuada de las FDA en las dos ciudades que concentran este mercado en Ecuador, y documenta diferencias intrapaís con implicaciones para estrategias de marketing diferenciadas.
Palabras clave:
Aplicaciones de delivery,
intención continuada,
UTAUT2,
confianza,
Covid-19,
Ecuador.
Códigos JEL:
M31, L81, D12, O33
Introduction/objective: the COVID-19 pandemic reshaped food consumption patterns worldwide. This study examines the attitudinal and behavioral changes reported by consumers in Ecuador’s two main cities (Quito and Guayaquil) toward food delivery apps (FDA) across three periods—pre-pandemic (2019), pandemic (2020-2021) and post-pandemic (2022-2025)—using retrospective questions.
Methodology: a cross-sectional study with a retrospective approach and non-experimental design. A questionnaire grounded in the extended UTAUT2 model with perceived trust and perceived COVID-19 risk was applied to a convenience sample of 468 consumers (n = 250 Quito; n = 218 Guayaquil). Data were analyzed using PLS-SEM and multi-group analysis (MGA).
Results: the model explains 67.3 % of the variance in continuance intention (R² = 0.673). The strongest significant predictors were habit (β = 0.52), performance expectancy (β = 0.41) and perceived trust (β = 0.29). City-level differences emerged: habit weighed more in Quito (β = 0.58 vs. 0.47) and social influence in Guayaquil (β = 0.46 vs. 0.31).
Conclusions: participants reported usage that remains above pre-pandemic levels. The study provides the first systematic empirical evidence on the determinants of FDA continuance intention in the two cities that concentrate this market in Ecuador, documenting intra-country differences with implications for differentiated marketing strategies.
Keywords:
Food delivery apps,
continuance intention,
UTAUT2,
trust,
COVID-19;
Ecuador.
Al-Essa, M. (2026). Examining consumers’ continuance intention to use P2P mobile payment systems: An extended TPB approach. Journal of Theoretical and Applied Electronic Commerce Research, 21(2), artículo 61. https://doi.org/10.3390/jtaer21020061
Al-Mamary, Y. H. (2026). Enabling digital transformation: Factors influencing continuance intention and use of digital government services in Saudi Arabia. Human Behavior and Emerging Technologies, 2026, artículo 5590676. https://doi.org/10.1155/hbe2/5590676
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185-216. https://doi.org/10.1177/135910457000100301
Cámara Ecuatoriana de Comercio Electrónico (CECE). (2024). Estudio de transacciones no presenciales en Ecuador – VII Medición. CECE.
Catacutan, Z. M. (2025). Filipinos’ e-wallet continuance usage intention and behavior: Deciphering the roles of task technology fit, UTAUT2 and trust using a SEM-ANN approach. Journal of Science and Technology Policy Management. Publicación anticipada en línea. https://doi.org/10.1108/JSTPM-09-2024-0373
Chakraborty, D. (2026). Exploring the drivers of continuance and recommendation intentions in online food delivery services: A technology continuance theory perspective. British Food Journal, 128(1), 407-425. https://doi.org/10.1108/BFJ-06-2025-0826
Chen, Y.-H., & Keng, K. (2026). Sustaining digital health engagement in emerging markets: A telemedicine study in post-COVID-19 Indonesia. Journal of Asia Business Studies, 20(3), 608-628. https://doi.org/10.1108/JABS-07-2025-0386
Chinelato, F. B., & Hoyos Vallejo, C. A. (2024). Operational excellence in online food delivery service: The role of food biosafety measures. British Food Journal, 126(12), 4485-4502. https://doi.org/10.1108/BFJ-05-2024-0455
De Borba, E. H., & Tezza, R. (2026). Playfulness as a key driver of behavioral intention in fashion mobile commerce. Journal of Global Fashion Marketing, 17(2), 133-149. https://doi.org/10.1080/20932685.2026.2629975
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1. Behavior Research Methods, 41(4), 1149-1160. https://doi.org/10.3758/BRM.41.4.1149
Gaber, H. R., Elbadrawy, R., Mostafa, L., & Oreiky, M. (2026). Consumer continuous intention to use of e-health services after COVID-19 pandemic: An extended UTAUT2 model. Cogent Business & Management, 13(1), artículo 2625533. https://doi.org/10.1080/23311975.2026.2625533
Golden, B. R. (1992). The past is the past–or is it? The use of retrospective accounts as indicators of past strategy. Academy of Management Journal, 35(4), 848-860. https://doi.org/10.5465/256318
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLSSEM) (2nd ed.). Sage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science,43 (1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431. https://doi.org/10.1108/IMR-09-2014-0304
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Hong, C., Choi, E.-K., & Joung, H.-W. (2023). Determinants of customer purchase intention toward online food delivery services: The moderating role of usage frequency. Journal of Hospitality and Tourism Management, 54, 76-87. https://doi.org/10.1016/j.jhtm.2022.12.005
Instituto Nacional de Estadística y Censos (INEC). (2023). Ecuador en cifras: estadísticas de población. https://www.ecuadorencifras.gob.ec/
Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 13-30. https://doi.org/10.1111/mono.12296
Joshi, R., Garg, P., Kumar, S., & Dhiman, N. (2025). What drives consumers to continually use food delivery apps? The moderating role of coupon proneness. Global Knowledge, Memory and Communication. Publicación anticipada en línea. https://doi.org/10.1108/GKMC-05-2024-0318
Khan, M. U., Nassar, S., Islam, M. F., Hossain, M. B., & Vasa, L. (2025). Is digitalization necessary for e-commerce adoption at small and medium-sized enterprises? The pandemic effects. Entrepreneurial Business and Economics Review, 13(4), 209-230. https://doi.org/10.15678/EBER.2025.130411
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: Extending UTAUT2 with information quality. Sustainability, 11(11), artículo 3141. https://doi.org/10.3390/su11113141
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114-121. https://doi.org/10.1037/0021-9010.86.1.114
Miller, C. C., Cardinal, L. B., & Glick, W. H. (1997). Retrospective reports in organizational research: A reexamination of recent evidence. Academy of Management Journal, 40(1), 189-204. https://doi.org/10.5465/257026
Munday, M., & Humbani, M. (2024). Determining the drivers of continued mobile food delivery app usage during a pandemic period. Cogent Business & Management, 11(1), 2308086. https://doi.org/10.1080/23311975.2024.2308086
Ortega-Vivanco, M. (2020). Efectos del Covid-19 en el comportamiento del consumidor: caso Ecuador. Retos. Revista de Ciencias de la Administración y Economía, 10(20), 233-247. https://doi.org/10.17163/ret.n20.2020.03
Ortiz-Prado, E., Henríquez-Trujillo, A. R., Rivera-Olivero, I. A., Lozada, T., & García-Bereguiain, M. A. (2021). High prevalence of SARS-CoV-2 infection among food delivery riders. A case study from Quito, Ecuador. Science of the Total Environment, 770, 145225. https://doi.org/10.1016/j.scitotenv.2021.145225
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54-74. https://doi.org/10.1037/0033-2909.124.1.54
Payments and Commerce Market Intelligence (PCMI). (2025). Ecuador’s e-commerce market: Trends and insights. https://paymentscmi.com/insights/ecuador-e-commerce-market/
Petroccione, G., & Taddei, M. (2026). Consumer behaviour and digital payments in the post-COVID-19 era: A systematic review and integrative framework for sustainable marketing. Competitiveness Review. Publicación anticipada en línea. https://doi.org/10.1108/CR-02-2026-0098
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Poon, W. C., & Tung, S. E. H. (2024). The rise of online food delivery culture during the COVID-19 pandemic: An analysis of intention and its associated risk. European Journal of Management and Business Economics, 33(1), 54-73. https://doi.org/10.1108/EJMBE-04-2021-0128
Puriwat, W., & Tripopsakul, S. (2021). Understanding food delivery mobile application technology adoption: A UTAUT model integrating perceived fear of COVID-19. Emerging Science Journal, 5(Special Issue), 94-104. https://doi.org/10.28991/esj-2021-SPER-08
Rahman, A., al Farid, F., Bashar, M. A., Uddin, J., Mahmud, A., & Karim, H. A. (2025). Examining the influence of deterrent and enhancement factors on QR-code mobile payment continuance intention: Insights from PLS-SEM and IPMA analysis. Frontiers in Big Data, 8, artículo 1679897. https://doi.org/10.3389/fdata.2025.1679897
Raza, S. A., Khan, K. A., & Hakim, F. (2023). Give your hunger a new option: Understanding consumers’ continuous intention to use online food delivery apps using trust transfer theory. International Journal of Consumer Studies, 47(1), 169-194. https://doi.org/10.1111/ijcs.12845
Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. El software estadístico más fácil de usar del mundo. SmartPLS. https://www.smartpls.com/
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
Valdivino, C. X., De Paula, T. M., & Gerhard, F. (2025). The use of e-commerce applications in Latin America: Individual and structural influences during COVID-19. Future Business Journal, 11(1), 1-14. https://doi.org/10.1186/s43093-025-00626-3 Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations, R. Journal of Statistical Software, 45(3), 1-67. https://doi.org/10.18637/jss.v045.i03
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Waris, I., & Suki, N. M. (2026). Unveiling the drivers of food delivery app continuance intention: Integrating expectation confirmation model and task-technology fit model. Journal of Internet Commerce, 25(2), 1-25. https://doi.org/10.1080/15332861.2026.2622613
Zanetta, L. D., Hakim, M. P., Gastaldi, G. B., Seabra, L. M. J., Rolim, P. M., Nascimento, L. G. P., Medeiros, C. O., & da Cunha, D. T. (2021). The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Research International 149, 110671. https://doi.org/10.1016/j.foodres.2021.110671
Instituciones
Universidad Espíritu Santo (UEES), Ecuador
Escuela Superior Politécnica del Litoral (ESPOL)
Universidad Ecotec, Ecuador
Universidad Internacional del Ecuador
Universitat de València, España
Copyright © 2026. Fundación Universitaria Konrad Lorenz, Colombia

