Understanding Predictors of Continued Usage Intention Toward Online Food Delivery Services Among Malaysian University Students: A Technology Acceptance Model Perspective

Authors

Saiful Nizam Warris

Department of Computer and Mathematical Sciences, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Penang (Malaysia)

Dahlan Abdullah

Faculty of Hospitality and Tourism Management, UCSI University, 56000 Kuala Lumpur (Malaysia)

Anderson Ngelambong

Faculty of Hotel and Tourism Management, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Penang (Malaysia)

Saidatul Syafiqah Athirah Ahmadrunizam

Faculty of Hotel and Tourism Management, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Penang (Malaysia)

Nor Fatin Aimi Sohaimi

Faculty of Hotel and Tourism Management, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Penang (Malaysia)

Article Information

DOI: 10.51244/IJRSI.2025.1210000074

Subject Category: Hospitality Management

Volume/Issue: 12/10 | Page No: 835-845

Publication Timeline

Submitted: 2025-10-02

Accepted: 2025-10-08

Published: 2025-11-04

Abstract

This study investigates the predictors influencing continued usage intention of Online Food Delivery Services (OFDS) among Malaysian university students. Grounded in the Technology Acceptance Model (TAM), the research explores the effects of perceived usefulness (PU), perceived ease of use (PEOU), variety of food choices (VFC), and electronic word of mouth (e-WOM) on behavioral intentions. Data were collected from 150 students through an online survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that PU, PEOU, and e-WOM significantly influence continued usage intention, whereas VFC does not show a statistically significant impact. These findings offer theoretical and practical implications for service providers aiming to retain younger digital consumers in a competitive food delivery market. The study highlights the importance of system usability and social influence in fostering long-term engagement with OFDS platforms.

Keywords

Online food delivery, Technology Acceptance Model, Continued usage intention, University students, Malaysia, e-WOM, Perceived usefulness

Downloads

References

1. Abdullah, D., Hambali, M. E. R. M., Kamal, S. B. M., Din, N., & Lahap, J. (2016). Factors influencing visual electronic word of mouth (e-WOM) on restaurant experience. In S. M. Radzi, M. H. M. Hanafiah, N. Sumarjan, & Z. Mohi (Eds.), Proceedings of the 3rd International Hospitality and Tourism Conference (IHTC 2016) & 2nd International Seminar on Tourism (ISOT 2016) (pp. 519–523). CRC Press. [Google Scholar] [Crossref]

2. Abdullah, D., Jayaraman, K., Shariff, D. N., Bahari, K. A., & Nor, N. M. (2017). The effects of perceived interactivity, perceived ease of use and perceived usefulness on online hotel booking intention: A conceptual framework. International Academic Research Journal of Social Science, 3(1), 16–23. https://doi.org/10.1016/S2212-5671(16)00079-4 [Google Scholar] [Crossref]

3. Abdullah, D., Kamal, S. B. M., Azmi, A., Lahap, J., Bahari, K. A., & Din, N. (2018). Perceived website interactivity, perceived usefulness and online hotel booking intention: A structural model. Malaysian Journal of Consumer and Family Economics, 21(S1). [Google Scholar] [Crossref]

4. Alghamdi, S. Y., Kaur, S., Qureshi, K. M., Almuflih, A. S., Almakayeel, N., Alsulamy, S., & Qureshi, M. R. N. (2023). Antecedents for online food delivery platform leading to continuance usage intention via e-word-of-mouth review adoption. PLoS ONE, 18(8 AUGUST), 1–18. https://doi.org/10.1371/journal.pone.0290247 [Google Scholar] [Crossref]

5. Ali, S., Khalid, N., Javed, H. M. U., & Islam, D. M. Z. (2021). Consumer adoption of online food delivery ordering (OFDO) services in Pakistan: The impact of the COVID-19 pandemic situation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1–23. https://doi.org/10.3390/joitmc7010010 [Google Scholar] [Crossref]

6. Bahari, K. A., Abdullah, D., Kamal, S. B. M., Johari, N. R., & Zulkafli, M. S. (2018). The influence of hotel website hotel website design quality, perceived ease of use, and perceived usefulness on loyalty intention. The Turkish Online Journal of Design, Art and Communication, September, 701–710. [Google Scholar] [Crossref]

7. Bao, Z., & Zhu, Y. (2022). Why customers have the intention to reuse food delivery apps: evidence from China. British Food Journal, 1(124). https://doi.org/https://doi.org/10.1108/BFJ-03-2021-0205 [Google Scholar] [Crossref]

8. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. http://www.jstor.org/stable/10.2307/249008 [Google Scholar] [Crossref]

9. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149 [Google Scholar] [Crossref]

10. Francioni, B., Curina, I., Hegner, S. M., & Cioppi, M. (2022). Predictors of continuance intention of online food delivery services: gender as moderator. International Journal of Retail and Distribution Management, 50(12), 1437–1457. https://doi.org/10.1108/IJRDM-11-2021-0537 [Google Scholar] [Crossref]

11. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). SAGE Publications Inc. [Google Scholar] [Crossref]

12. Hong, C., Choi, H. (Hailey), Choi, E. K. (Cindy), & Joung, H. W. (David). (2021). Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. Journal of Hospitality and Tourism Management, 48(April), 509–518. https://doi.org/10.1016/j.jhtm.2021.08.012 [Google Scholar] [Crossref]

13. Jun, K., Yoon, B., Lee, S., & Lee, D. S. (2022). Factors influencing customer decisions to use online food delivery service during the covid-19 pandemic. Foods, 11(1). https://doi.org/10.3390/foods11010064 [Google Scholar] [Crossref]

14. Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468. https://doi.org/10.1016/j.tourman.2007.05.011 [Google Scholar] [Crossref]

15. Marzuki, M. I. I., Rosly, A. N., Roslan, N. S., Abdullah, D., Kamal, S. B. M., & Azmi, A. (2016). The role of perceived interactivity, perceived ease of use, perceived usefulness, and perceived enjoyment toward intention to use online mapping service applications. International Academic Research Journal of Business and Technology, 2(2), 135–139. [Google Scholar] [Crossref]

16. Mohamad, M. A., Radzi, S. M., & Hanafiah, M. H. (2021). Understanding tourist mobile hotel booking behaviour: Incorporating perceived enjoyment and perceived price value in the modified Technology Acceptance Model. Tourism and Management Studies, 17(1), 19–30. https://doi.org/10.18089/TMS.2021.170102 [Google Scholar] [Crossref]

17. Nonis, M. F. (2022). The Integration of Technology Acceptance Model (TAM) And Theory of Planned Behavior (TPB) on Online Purchase Intention of Shopee Marketplace Consumers. Asia Proceedings of Social Sciences, 3(1), 15–31. https://readersinsight.net/APSS/article/view/2236 [Google Scholar] [Crossref]

18. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. SmartPLS. [Google Scholar] [Crossref]

19. Shin, J., Moon, S., Cho, B. ho, Hwang, S., & Choi, B. (2022). Extended technology acceptance model to explain the mechanism of modular construction adoption. Journal of Cleaner Production, 342(January). https://doi.org/10.1016/j.jclepro.2022.130963 [Google Scholar] [Crossref]

20. Statista. (2025). Online meal delivery Malaysia. https://www.statista.com/outlook/emo/online-food-delivery/meal-delivery/malaysia [Google Scholar] [Crossref]

21. Suhartanto, D., Helmi Ali, M., Tan, K. H., Sjahroeddin, F., & Kusdibyo, L. (2019). Loyalty toward online food delivery service: the role of e-service quality and food quality. Journal of Foodservice Business Research, 22(1), 81–97. https://doi.org/10.1080/15378020.2018.1546076 [Google Scholar] [Crossref]

22. Troise, C., O’Driscoll, A., Tani, M., & Prisco, A. (2021). Online food delivery services and behavioural intention – a test of an integrated TAM and TPB framework. British Food Journal, 123(2), 664–683. https://doi.org/10.1108/BFJ-05-2020-0418 [Google Scholar] [Crossref]

23. Wang, O., & Scrimgeour, F. (2022). Consumer adoption of online-to-offline food delivery services in China and New Zealand. British Food Journal, 124(5), 1590–1608. https://doi.org/10.1108/BFJ-03-2021-0208 [Google Scholar] [Crossref]

24. Wang, O., Somogyi, S., & Charlebois, S. (2020). Food choice in the e-commerce era : A comparison between business-to-consumer (B2C), online-to-offline (O2O) and new retail. British Food Journal, 122(4), 1215–1237. https://doi.org/10.1108/BFJ-09-2019-0682 [Google Scholar] [Crossref]

25. Waris, I., Ali, R., Nayyar, A., Baz, M., Liu, R., & Hameed, I. (2022). An empirical evaluation of customers’ adoption of drone food delivery services: An extended Technology Acceptance Model. Sustainability (Switzerland), 14(5), 1–18. https://doi.org/10.3390/su14052922 [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles