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
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 lately influence their intention to use online food delivery (OFD) apps via e-commerce platforms. Researcher has used a quantitative and exploratory approach to analyze answer collected from survey, 300 questionnaires had been used for survey and 287 ones were collected to evaluate the testing model applied (TAM and TPB Model) using Partial Least Squares Structural Equation Model (Smart-PLS). The results yield that the Easy Payment Method exhibits a moderate degree of impact whereas Time saving and Social influence just constitutes weak significance on behavioral intention to use online food delivery. The outbreak of Covid-19 have forced businesses to change their traditional way of operation as by trying to apply technology into the modern model of food order to carter the rising need of consumers. So that, this research also give companies some recommendations on latent factors that can affect consumer intention to use the online delivery service for F & B products.
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
References
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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.
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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.
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.
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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.
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.
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.
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133.
Sultan, M. U., & Uddin, M. D. N. (2011). Consumers’ Attitude towards Online Shopping: Factors influencing Gotland consumers to shop online.
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.
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.
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.
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.
Al-Fahim, N. H., Ateeq, A. A., Abro, Z., Milhem, M., Alzoraiki, M., Alkadash, T. M., & Nagi, M. (2024). Factors influencing the mobile banking usage: mediating role of perceived usefulness. In Digital technology and changing roles in managerial and financial accounting: theoretical knowledge and practical application (Vol. 36, pp. 115–128). Emerald Publishing Limited.
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.
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.
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.
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.
Chai, L. T., & Yat, D. N. C. (2019). Online food delivery services: Making food delivery the new normal. Journal of Marketing Advances and Practices, 1(1), 62–77.
Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychol. Bull., 112, 1155–1159.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
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.
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.
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107.
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.
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.
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.
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.
Islam, S. (2024). Impact of online payment systems on customer trust and loyalty in E-commerce analyzing security and convenience. Available at 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).
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.
Kedah, Z. (2023). Use of e-commerce in the world of business. Startupreneur Business Digital (SABDA Journal), 2(1), 51–60.
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).
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.
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.
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.
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.
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.
Network, E.-V. B. (2018). E-commerce industry in Vietnam. EU-Vietnam Business Network (EVBN), Ho Chi Minh City.
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.
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.
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.
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133.
Sultan, M. U., & Uddin, M. D. N. (2011). Consumers’ Attitude towards Online Shopping: Factors influencing Gotland consumers to shop online.
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.
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.
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.
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.
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://jfm.edu.vn/index.php/jfme/article/view/1017