Convenience Motivation, Hedonic Motivation, and Information Quality as Drivers of Online Food Delivery Service Adoption in Malaysia
Authors
School of Business, Asia Pacific University of Technology & Innovation Bukit Jalil, Kuala Lumpur (Malaysia)
School of Business, Asia Pacific University of Technology & Innovation Bukit Jalil, Kuala Lumpur (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.10200454
Subject Category: Digital Marketing
Volume/Issue: 10/2 | Page No: 6161-6173
Publication Timeline
Submitted: 2026-02-21
Accepted: 2026-02-27
Published: 2026-03-16
Abstract
The purpose of this study is to investigate the effects of hedonic motivation, convenience motivation, and information quality on consumers' intention to use online food delivery services in Malaysia. A total of 168 respondents participated in this study. Data were collected using a convenience sampling method. The findings revealed that hedonic motivation, convenience motivation, and information quality are positively related to the intention to use online food delivery services in Malaysia. Interestingly, this study uncovered that hedonic motivation has the highest effect on consumers’ intention to use online food delivery services. This shows that the relevant stakeholders, including platform providers, restaurant owners, and marketers, should focus on developing a platform that tends to invoke enjoyable and pleasurable feelings. This can be done through exciting promotions, entertaining games or features, or even providing customizable features according to consumers’ preferences, so that when consumers experience pleasure or enjoyment, they are more likely to continue utilizing the platform, resulting in increased use and profitability for the stakeholders.
Keywords
Convenience Motivation, Hedonic Motivation, Information Quality, Intention to Use, Food Delivery
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References
1. Abbasi, A. Z., Mirza, F., Albashrawi, M., Ting, D. H., & Abbasi, G. A. (2025). Hedonic consumption of virtual rides entertainment service: a multi-method perspective: SEM and fsQCA. Kybernetes, 54(14), 7548-7572. https://doi.org/10.1108/K-11-2023-2338 [Google Scholar] [Crossref]
2. Abbasi, G. A., Sandran, T., Ganesan, Y., & Iranmanesh, M. (2022). Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA. Technology in Society, 68, 101937. https://doi.org/10.1016/j.techsoc.2022.101937 [Google Scholar] [Crossref]
3. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t [Google Scholar] [Crossref]
4. Al Amin, M., Arefin, M. S., Alam, M. R., Ahammad, T., & Hoque, M. R. (2021). Using mobile food delivery applications during COVID-19 pandemic: An extended model of planned behavior. Journal of Food Products Marketing, 27(2), 105-126. https://doi.org/10.1080/10454446.2021.1906817 [Google Scholar] [Crossref]
5. Allah Pitchay, A., Ganesan, Y., Zulkifli, N. S., & Khaliq, A. (2022). Determinants of customers' intention to use online food delivery application through smartphone in Malaysia. British Food Journal, 124(3), 732-753. https://doi.org/10.1108/BFJ-01-2021-0075 [Google Scholar] [Crossref]
6. Ali, S., Khalid, N., Javed, H. M. U., & Islam, D. M. Z. (2020). 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), 10. https://doi.org/10.3390/joitmc7010010 [Google Scholar] [Crossref]
7. Ariffin, S., Abdul Manan, H., Ahmad, N., Muhammad, N. S., Hamdan, F., & S Kelana, N. S. (2021). Continuous intention to use technology of online food delivery services among young adults. Advances in Business Research International Journal, 7(1), 56-64. [Google Scholar] [Crossref]
8. Blut, M., & Wang, C. (2020). Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of marketing science, 48(4), 649-669. https://doi.org/10.1007/s11747-019-00680-8 [Google Scholar] [Crossref]
9. Christino, J. M. M., Cardozo, Érico A. A., Petrin, R., & Pinto, L. H. de A. (2021). Factors Influencing the Intent and Usage Behavior of Restaurant Delivery Apps. Review of Business Management, 23(1), 21–42. https://doi.org/10.7819/rbgn.v23i1.4095 [Google Scholar] [Crossref]
10. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 [Google Scholar] [Crossref]
11. Dirin, A., Nieminen, M., & Laine, T. H. (2023). Feelings of being for mobile user experience design. International Journal of Human–Computer Interaction, 39(20), 4059-4079. https://doi.org/10.1080/10447318.2022.2108964 [Google Scholar] [Crossref]
12. Dutta, K., Pookulangara, S., Wen, H., Josiam, B., & Parsa, H. G. (2025). Hedonic and utilitarian motivations and the role of trust in using food delivery apps: an investigation from a developing economy. Journal of Foodservice Business Research, 1-25. https://doi.org/10.1080/15378020.2024.2440677 [Google Scholar] [Crossref]
13. Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7 [Google Scholar] [Crossref]
14. Gupta, P., Prashar, S., Vijay, T. S., & Parsad, C. (2021). Examining the influence of antecedents of continuous intention to use an informational app: the role of perceived usefulness and perceived ease of use. International Journal of Business Information Systems, 36(2), 270-287. https://doi.org/10.1504/IJBIS.2021.112829 [Google Scholar] [Crossref]
15. Fathy, E. A., Salem, I. E., Zidan, H. A. K. Y., & Abdien, M. K. (2025). From plate to post: how foodstagramming enriches tourist satisfaction and creates memorable experiences in culinary tourism. Current Issues in Tourism, 28(20), 3373-3392. 10.1080/13683500.2024.2405625 [Google Scholar] [Crossref]
16. 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 [Google Scholar] [Crossref]
17. Ghanbary, S., Sharifi, S. M., & Momeni, S. (2022). OFD platform: consumers’ persuasion based on economic, relational and enjoyment values. Journal of Foodservice Business Research, 25(3), 329-352. 10.1080/15378020.2021.1950509 [Google Scholar] [Crossref]
18. Haikal. (2021). 87% of consumers will keep ordering food online: Grab report. The Vibes. https://www.thevibes.com/articles/lifestyles/46147/87-of-consumers-will-keep-ordering-food-online-grab-report [Google Scholar] [Crossref]
19. Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi-org/10.1016/j.rmal.2022.100027 [Google Scholar] [Crossref]
20. Hooi, R., Leong, T. K., & Yee, L. H. (2021, March). Intention to use online food delivery service in Malaysia among university students. In CoMBInES-Conference on Management, Business, Innovation, Education and Social Sciences, 1 (1), 60-73.10.6007/IJARBSS/v12-i12/15428 [Google Scholar] [Crossref]
21. Humaidi, N. F. A., Mohd Jamil, S. N., Gannasin, S. P., & Samat, N. (2024). Enhancing users’ satisfaction: revealing the key factors influencing the usage of online food delivery service applications among university students. Journal of Tourism, Hospitality and Culinary Arts, 16(1), 993-1010. [Google Scholar] [Crossref]
22. Idris, N. A., Mohamad, M. A., Manshoor, A., Mohamad, N. H., & Che Ngah, H. (2021). Consumers’ intention towards online food ordering and delivery service. Jurnal Intelek, 16(2), 37-47. [Google Scholar] [Crossref]
23. Keszey, T. (2020). Behavioural intention to use autonomous vehicles: Systematic review and empirical extension. Transportation Research Part C: Emerging Technologies, 119, 102732. https://doi.org/10.1016/j.trc.2020.102732 [Google Scholar] [Crossref]
24. Martono, S., Nurkhin, A., Mukhibad, H., Anisykurlillah, I., & Wolor, C. W. (2020). Understanding the Employee’s Intention to Use Information System: Technology Acceptance Model and Information System Success Model Approach. The Journal of Asian Finance, Economics and Business, 7(10), 1007–1013. https://doi.org/10.13106/jafeb.2020.vol7.no10.1007 [Google Scholar] [Crossref]
25. Mohamed, R. N., Sawangchai, A., Rusli, M. S., & Borhan, H. (2022). Factors influencing the online food delivery services apps on purchase intention among customers in Klang Valley, Malaysia during COVID-19. Journal of Marketing Management and Consumer Behavior, 4(1). [Google Scholar] [Crossref]
26. Muangmee, C., Kot, S., Meekaewkunchorn, N., Kassakorn, N., & Khalid, B. (2021). Factors Determining the Behavioral Intention of Using Food Delivery Apps during COVID-19 Pandemics. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1297–1310. https://doi.org/10.3390/jtaer16050073 [Google Scholar] [Crossref]
27. Nayan, N. M., & Hassan, M. K. A. (2020). Customer satisfaction evaluation for online food service delivery system in Malaysia. J. Inf. Syst. Technol. Manag, 5(9), 123-136. 10.35631/JISTM.5190010 [Google Scholar] [Crossref]
28. Nguyen, T., & Nguyen, D. M. (2024). What will make Generation Y and Generation Z to continue to use online food delivery services: a uses and gratifications theory perspective. Journal of Hospitality Marketing & Management, 33(4), 415-442. https://doi.org/10.1080/19368623.2023.2277731 [Google Scholar] [Crossref]
29. Ningtyas, D. A. K., & Kurniawan, D. T. (2024, June). The Influence of Perceived Usefulness, Perceived Ease of Use, and Electronic Word of Mouth on Customer Loyalty Through Repurchase Intention in the TikTok Shop. In 4th International Conference on Halal Development (4th ICHaD 2023) (pp. 217-230). Atlantis Press. [Google Scholar] [Crossref]
30. Novita, D., & Husna, N. (2020). The influence factors of consumer behavioral intention towards online food delivery services. TECHNOBIZ: International Journal of Business, 3(2), 40-42. [Google Scholar] [Crossref]
31. Peng, M. Y. P., & Yan, X. (2022). Exploring the influence of determinants on behavior intention to use of multiple media kiosks through technology readiness and acceptance model. Frontiers in Psychology, 13, 852394. https://doi.org/10.3389/fpsyg.2022.852394 [Google Scholar] [Crossref]
32. Prasetyo, Y. T., Tanto, H., Mariyanto, M., Hanjaya, C., Young, M. N., Persada, S. F., & Redi, A. A. N. P. (2021). Factors affecting customer satisfaction and loyalty in online food delivery service during the COVID-19 pandemic: Its relation with open innovation. Journal of open innovation: technology, market, and complexity, 7(1), 76. https://doi.org/10.3390/joitmc7010076 [Google Scholar] [Crossref]
33. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students Eighth Edition. England: Pearson Education Limited. [Google Scholar] [Crossref]
34. Sekaran, U., & Bougie, R. (2016). Research Methods For Business: A Skill Building Approach, 7th Edition. John Wiley & Sons Ltd. [Google Scholar] [Crossref]
35. Shankar, A. (2021). How does convenience drive consumers' webrooming intention?. International Journal of Bank Marketing, 39(2), 312-336. https://doi.org/10.1108/IJBM-03-2020-0143 [Google Scholar] [Crossref]
36. Statista, (2025). Online Food Delivery – Worldwide. Statista. https://www.statista.com/outlook/emo/online-food-delivery/worldwide [Google Scholar] [Crossref]
37. Tarmazi, S. A. A., Ismail, W. R. W., Azmin, N. A. S. N., & Bakar, A. R. A. (2021). Consumer purchase intention toward online food delivery service: The implication for future research. Malaysian Journal of Social Sciences and Humanities (MJSSH), 6(9), 347-354. [Google Scholar] [Crossref]
38. Vermeir, I., & Roose, G. (2020). Visual design cues impacting food choice: A review and future research agenda. Foods, 9(10), 1495. https://doi.org/10.3390/foods9101495 [Google Scholar] [Crossref]
39. Viktor. (2020). The Food Delivery Business Model – A Complete Guide. Productmint. https://productmint.com/the-food-delivery-business-model-a-complete-guide/ [Google Scholar] [Crossref]
40. Wang, J., Mustaffa, N, B., Mahbob, M, H, B. (2025) The Impact of Visual Communication in Packaging Design on Consumer Purchase Behaviour: A Case-Based Analysis. International Journal of Instructional Cases, 9(1), 1-24. [Google Scholar] [Crossref]
41. Wiastuti, R. D., Prawira, O., Lusyana, L., Lestari, N. S., Masatip, A., & Ngatemin, N. (2022). The Relationship Between Convenience Motivation, Attitude, And Behavioral Intention Of Food Delivery Applications’ Users. GeoJournal of Tourism and Geosites, 41(2), 548–554. https://doi.org/10.30892/gtg.41228-862 [Google Scholar] [Crossref]
42. Widagdo, B., & Roz, K. (2021). Hedonic Shopping Motivation and Impulse Buying: The Effect of Website Quality on Customer Satisfaction. The Journal of Asian Finance, Economics and Business, 8(1), 395–405. https://doi.org/10.13106/jafeb.2021.vol8.no1.395 [Google Scholar] [Crossref]
43. Wu, L. L., Liu, S. Q., Huang, H., & Yu, X. (2021). Photo vs. art? The design of consumption guidance in cultural food consumption. International Journal of Hospitality Management, 97, 103008. 10.1016/j.ijhm.2021.103008 [Google Scholar] [Crossref]
44. Yeh, C. W., & Chen, T. Y. (2024). The role of online game usage in the relationship between initial daily negative moods and subsequent positive moods: The moderating role of hedonistic motivation. Current Psychology, 43(7), 6101-6113. [Google Scholar] [Crossref]
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