Factors influencing Generation Z’s behavioral intention to use food delivery service: A case study of Ho Chi Minh City
0
Online Published:
28/11/2025
Section:
Business Administration, Marketing, Commerce, and Tourism
Main Article Content
Abstract
The outbreak of Covid-19 forced businesses to change their traditional way of operation by trying to apply technology into the modern model of food ordering to cater to the rising needs of consumers. In this context, this research examines hypotheses about 3 factors: time saving, social influence and easy payment method which affect consumer perceived ease to use and perceived usefulness, and ultimately influence their intention to use online food delivery (OFD) apps via e-commerce platforms. The researcher has used a quantitative and exploratory approach to analyze answers collected from a survey; 300 questionnaires were used, and 287 valid ones were collected to evaluate the testing model applied (Extended TAM and TPB Model) using Partial Least Squares Structural Equation Modeling (Smart-PLS). The results yield that the Easy Payment Method exhibits the strongest impact (f² = 0.333) whereas Time saving and Social influence exhibit weak but significant impacts on behavioral intention to use online food delivery. The model achieved a moderate explanatory power for Intention to Use (R² = 0.481). Therefore, this research recommends F&B companies prioritize simplified payment systems, streamline time-saving features, and leverage social influence marketing to enhance consumer adoption of online food delivery services.
Keywords
Food delivery services; Generation Z; Behavioral intention; Consumer behavior; Technology Acceptance Model
Article Details
Field of Economic (JEL Codes)
D91 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making - Micro-Based Behavioral Economics, L81 - Retail and Wholesale Trade • e-Commerce - Industry Studies: Services, M31 - Marketing - Marketing and Advertising
References
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Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
Cohen, J. (1988). The effect size. Statistical Power Analysis for the Behavioral Sciences, 77–83. https://doi.org/10.4324/9780203771587
Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychol. Bull., 112, 1155–1159. https://doi.org/10.1037/0033-2909.112.1.155
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008
Dazmin, D., & Ho, M. Y. (2019). The relationship between consumers’ price-saving orientation and time-saving orientation towards food delivery intermediaries (FDI) services: an exploratory study. Gsj, 7(2), 175–190.
Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(4), 854–887. https://doi.org/10.1108/IntR-03-2018-0089
Foroughi, B., Yadegaridehkordi, E., Iranmanesh, M., Sukcharoen, T., Ghobakhlo, M., & Nilashi, M. (2024). Determinants of continuance intention to use food delivery apps: findings from PLS and fsQCA. International Journal of Contemporary Hospitality Management, 36(4), 1235–1261. https://doi.org/10.1108/IJCHM-10-2022-1209
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair, J., Ringle, C., & Sarstedt, M. (2016). Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3. Academy of Marketing Science (AMS) Annual Conference, 1–3.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
Islam, S. (2024). Impact of online payment systems on customer trust and loyalty in E-commerce analyzing security and convenience. Available at SSRN 5064838. http://dx.doi.org/10.2139/ssrn.5064838
Kajandren, R., Ariffin, N. A. B., Manimaran, P., Maniselvan, D., Ravindran, V., & Kanapathipillai, K. (2023). AN ANALYSIS OF THE FACTORS THAT IMPACT THE CUSTOMERS’SATISFACTION ON ONLINE FOOD DELIVERY SERVICES IN PETALING JAYA, SELANGOR, MALAYSIA. European Journal of Social Sciences Studies, 8(4). https://doi.org/10.46827/ejsss.v8i4.1422
Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building construction based on technology acceptance model (TAM) and technology, organization, and environment (TOE) framework. Journal of Building Engineering, 45, 103518. https://doi.org/10.1016/j.jobe.2021.103518
Kedah, Z. (2023). Use of e-commerce in the world of business. Startupreneur Business Digital (SABDA Journal), 2(1), 51–60. https://doi.org/10.33050/sabda.v2i1.273
Kurniawan, A. C., Rachmawati, N. L., Ayu, M. M., Ong, A. K. S., & Redi, A. A. N. P. (2024). Determinants of satisfaction and continuance intention towards online food delivery service users in Indonesia post the COVID-19 pandemic. Heliyon, 10(1). http://doi.org/10.1016/j.heliyon.2023.e23298
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), 3141. https://doi.org/10.3390/su11113141
Lee, W. S., Song, M., Moon, J., & Tang, R. (2023). Application of the technology acceptance model to food delivery apps. British Food Journal, 125(1), 49–64. https://doi.org/10.1108/BFJ-05-2021-0574
Leung, Y. L., Chan, R. L. H., Chiu, D. K. W., & Ruwen, T. (2025). Investigating the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic. Aslib Journal of Information Management, 77(2), 330–353. https://doi.org/10.1108/AJIM-05-2023-0177
Lin, P. M. C., Au, W. C. W., & Baum, T. (2024). Service quality of online food delivery mobile application: an examination of the spillover effects of mobile app satisfaction. International Journal of Contemporary Hospitality Management, 36(3), 906–926. https://doi.org/10.1108/IJCHM-09-2022-1103
Mahpour, A., Baghestani, A., & Mollajani, M. (2024). The lasting legacy of COVID-19 on individual shopping decisions: tracing the evolution of online shopping habits and its stability. Transportation Planning and Technology, 1–21. https://doi.org/10.1080/03081060.2024.2399632
Nguyen, M. (2019). How food delivery services in Vietnam accommodate milennials and generation Z: Case company: Delivery now.
Paudel, R., Tehrani, S., & Waris, M. Z. (2024). Factors that influence the behavioral intention of customers toward online food delivery service. TransAmerica Review, 2(2), e24008–e24008. https://doi.org/10.62910/transame24008
Peng, L., Adeel, I., Ayub, A., & Rasool, Z. (2024). Investigating the roles of word of mouth and brand image between social media marketing activities and brand equity. Sage Open, 14(1), 21582440231220110. https://doi.org/10.1177/21582440231220113
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 197–217). Springer. https://doi.org/10.1007/978-3-319-64069-3_9
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Sultan, M. U., & Uddin, M. D. N. (2011). Consumers’ Attitude towards Online Shopping: Factors influencing Gotland consumers to shop online.
Tan, S. Y., Lim, S. Y., & Yeo, S. F. (2024). Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic. F1000Research, 10(972), 972. https://doi.org/10.12688/f1000research.73014.2
Teo, S. C., Liew, T. W., & Lim, H. Y. (2024). Factors influencing consumers’ continuance purchase intention of local food via online food delivery services: the moderating role of gender. Cogent Business & Management, 11(1), 2316919. https://doi.org/10.1080/23311975.2024.2316919
Toros, E., Asiksoy, G., & Sürücü, L. (2024). Refreshment students’ perceived usefulness and attitudes towards using technology: a moderated mediation model. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-024-02839-3
Tran, V. D. (2021). Using mobile food delivery applications during the COVID-19 pandemic: applying the theory of planned behavior to examine continuance behavior. Sustainability, 13(21), 12066. https://doi.org/10.3390/su132112066
Zhao, Y., Papier, F., & Teo, C.-P. (2024). Market thickness in online food delivery platforms: The impact of food processing times. Manufacturing & Service Operations Management, 26(3), 853–872. https://doi.org/10.1287/msom.2021.0354
An, S., Eck, T., & Yim, H. (2023). Understanding consumers’ acceptance intention to use mobile food delivery applications through an extended technology acceptance model. Sustainability, 15(1), 832. https://doi.org/10.3390/su15010832
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https://doi.org/10.1037/0033-2909.103.3.411
Arli, D., van Esch, P., & Weaven, S. (2024). The impact of SERVQUAL on consumers’ satisfaction, loyalty, and intention to use online food delivery services. Journal of Promotion Management, 30(7), 1159–1188. https://doi.org/10.1080/10496491.2024.2372858
Ayhün, S. E., Köse, B. Ç., & Aydin, E. (2024). Determinants of Customers’ Intention to Use Online Food Delivery Services: A Study of Generation Z. WSEAS Transactions on Business and Economics, 21, 1642–1656. https://doi.org/10.37394/23207.2024.21.134
Bahari, K. A., Abdullah, D., Wahab, J., Kamal, S. B. M., Johari, N. R., & Zulkafli, M. S. (2024). The Factors Influencing A Customer’s Purchase Intention on Online Shopping Website. European Proceedings of Social and Behavioural Sciences. https://doi.org/10.15405/epsbs.2024.05.12
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research (pp. 295–336). Psychology Press. https://doi.org/10.4324/9781410604385-10
Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
Cohen, J. (1988). The effect size. Statistical Power Analysis for the Behavioral Sciences, 77–83. https://doi.org/10.4324/9780203771587
Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychol. Bull., 112, 1155–1159. https://doi.org/10.1037/0033-2909.112.1.155
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008
Dazmin, D., & Ho, M. Y. (2019). The relationship between consumers’ price-saving orientation and time-saving orientation towards food delivery intermediaries (FDI) services: an exploratory study. Gsj, 7(2), 175–190.
Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(4), 854–887. https://doi.org/10.1108/IntR-03-2018-0089
Foroughi, B., Yadegaridehkordi, E., Iranmanesh, M., Sukcharoen, T., Ghobakhlo, M., & Nilashi, M. (2024). Determinants of continuance intention to use food delivery apps: findings from PLS and fsQCA. International Journal of Contemporary Hospitality Management, 36(4), 1235–1261. https://doi.org/10.1108/IJCHM-10-2022-1209
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair, J., Ringle, C., & Sarstedt, M. (2016). Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3. Academy of Marketing Science (AMS) Annual Conference, 1–3.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
Islam, S. (2024). Impact of online payment systems on customer trust and loyalty in E-commerce analyzing security and convenience. Available at SSRN 5064838. http://dx.doi.org/10.2139/ssrn.5064838
Kajandren, R., Ariffin, N. A. B., Manimaran, P., Maniselvan, D., Ravindran, V., & Kanapathipillai, K. (2023). AN ANALYSIS OF THE FACTORS THAT IMPACT THE CUSTOMERS’SATISFACTION ON ONLINE FOOD DELIVERY SERVICES IN PETALING JAYA, SELANGOR, MALAYSIA. European Journal of Social Sciences Studies, 8(4). https://doi.org/10.46827/ejsss.v8i4.1422
Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building construction based on technology acceptance model (TAM) and technology, organization, and environment (TOE) framework. Journal of Building Engineering, 45, 103518. https://doi.org/10.1016/j.jobe.2021.103518
Kedah, Z. (2023). Use of e-commerce in the world of business. Startupreneur Business Digital (SABDA Journal), 2(1), 51–60. https://doi.org/10.33050/sabda.v2i1.273
Kurniawan, A. C., Rachmawati, N. L., Ayu, M. M., Ong, A. K. S., & Redi, A. A. N. P. (2024). Determinants of satisfaction and continuance intention towards online food delivery service users in Indonesia post the COVID-19 pandemic. Heliyon, 10(1). http://doi.org/10.1016/j.heliyon.2023.e23298
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), 3141. https://doi.org/10.3390/su11113141
Lee, W. S., Song, M., Moon, J., & Tang, R. (2023). Application of the technology acceptance model to food delivery apps. British Food Journal, 125(1), 49–64. https://doi.org/10.1108/BFJ-05-2021-0574
Leung, Y. L., Chan, R. L. H., Chiu, D. K. W., & Ruwen, T. (2025). Investigating the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic. Aslib Journal of Information Management, 77(2), 330–353. https://doi.org/10.1108/AJIM-05-2023-0177
Lin, P. M. C., Au, W. C. W., & Baum, T. (2024). Service quality of online food delivery mobile application: an examination of the spillover effects of mobile app satisfaction. International Journal of Contemporary Hospitality Management, 36(3), 906–926. https://doi.org/10.1108/IJCHM-09-2022-1103
Mahpour, A., Baghestani, A., & Mollajani, M. (2024). The lasting legacy of COVID-19 on individual shopping decisions: tracing the evolution of online shopping habits and its stability. Transportation Planning and Technology, 1–21. https://doi.org/10.1080/03081060.2024.2399632
Nguyen, M. (2019). How food delivery services in Vietnam accommodate milennials and generation Z: Case company: Delivery now.
Paudel, R., Tehrani, S., & Waris, M. Z. (2024). Factors that influence the behavioral intention of customers toward online food delivery service. TransAmerica Review, 2(2), e24008–e24008. https://doi.org/10.62910/transame24008
Peng, L., Adeel, I., Ayub, A., & Rasool, Z. (2024). Investigating the roles of word of mouth and brand image between social media marketing activities and brand equity. Sage Open, 14(1), 21582440231220110. https://doi.org/10.1177/21582440231220113
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 197–217). Springer. https://doi.org/10.1007/978-3-319-64069-3_9
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Sultan, M. U., & Uddin, M. D. N. (2011). Consumers’ Attitude towards Online Shopping: Factors influencing Gotland consumers to shop online.
Tan, S. Y., Lim, S. Y., & Yeo, S. F. (2024). Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic. F1000Research, 10(972), 972. https://doi.org/10.12688/f1000research.73014.2
Teo, S. C., Liew, T. W., & Lim, H. Y. (2024). Factors influencing consumers’ continuance purchase intention of local food via online food delivery services: the moderating role of gender. Cogent Business & Management, 11(1), 2316919. https://doi.org/10.1080/23311975.2024.2316919
Toros, E., Asiksoy, G., & Sürücü, L. (2024). Refreshment students’ perceived usefulness and attitudes towards using technology: a moderated mediation model. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-024-02839-3
Tran, V. D. (2021). Using mobile food delivery applications during the COVID-19 pandemic: applying the theory of planned behavior to examine continuance behavior. Sustainability, 13(21), 12066. https://doi.org/10.3390/su132112066
Zhao, Y., Papier, F., & Teo, C.-P. (2024). Market thickness in online food delivery platforms: The impact of food processing times. Manufacturing & Service Operations Management, 26(3), 853–872. https://doi.org/10.1287/msom.2021.0354
How to Cite
Ha, M. H., Nguyen, P. H. A., & Pham, T. T. T. (2025). Factors influencing Generation Z’s behavioral intention to use food delivery service: A case study of Ho Chi Minh City. Journal of Finance - Marketing Research, 4(1en). https://doi.org/10.52932/jfmr.v4i1en.1017