Factors Influencing University Students Using the Online Food Delivery in Malaysia

Pauline Claudia Hutabarat, Celine Tan, Tan Yan Shing, Tee Wei Seng


In this modern era, people need to find efficient solutions, especially for university students who have limited time constraints. This research paper will study the correlation between factors such as performance expectancy, effort expectancy, social influence, information quality, time-saving orientation, and price-saving orientation toward intention to use online food delivery for university students specifically in Malaysia. This paper adopted a framework from a previous research paper by Pitchay et al., (2021) which talks about topics that are in line but with a wider range of respondents. Respondents for this research paper are university students. Researchers want to test what influences this demographic to use online food delivery services. This paper also comes with solutions that can help the industry to enhance its business and attract more consumers. This paper uses the convenience sampling method to obtain data from various respondents; this is also due to the limitations of money and time in working on this research paper.


Adroit Market Research. (2019). Malaysia Online Food Delivery Market by Type and Region Global Forecast 2021 to 2028. https://www.adroitmarketresearch.com/industry-reports/malaysia-online-food-delivery-market

Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. https://doi.org/10.1145/375663.375668

Ahn, T., Ryu, S., & Han, I. (2007, April). The impact of Web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263 - 275. https://doi.org/10.1016/j.im.2006.12.008

Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28–44. https://doi.org/10.1016/j.ijinfomgt.2019.04.008

Albers, M. J. (2017). Quantitative data analysis—In the graduate curriculum. Journal of Technical Writing and Communication, 47(2), 215-233.

Almanasreh, E., Moles, R., & Chen, T. F. (2019). Evaluation of methods used for estimating content validity. Research in social & administrative pharmacy : RSAP, 15(2), 214–221. https://doi.org/10.1016/j.sapharm.2018.03.066

Altinay, L., Paraskevas, A., & Jang, S. (2016). Planning research in hospitality and tourism. Routledge.

Amankwaa, L. (2016). Creating protocols for trustworthiness in qualitative research, Journal of Cultural Diversity, 23(3), 121-127. https://web.s.ebscohost.com/ehost/detail/detail?vid=0&sid=07a2042b-090c-4495-8eb0-caa4bfd396b2%40redis&bdata=JkF1dGhUeXBlPXNoaWImc2l0ZT1laG9zdC1saXZlJnNjb3BlPXNpdGU%3d#AN=118362617&db=asn

Anastasia, A. (1988). Psychological testing (6th ed.). New York: Macmillan Publishing.

Bashir, R., Mehboob, I., & Bhatti, W. K. (2015). Effects of online shopping trends on consumer- buying Behavior: an empirical study of Pakistan, Journal of Management and Research, 2(2), 1-25.

Blair, E. (2015). A reflexive exploration of two qualitative data coding techniques. Journal of Methods and Measurement in the Social Sciences, 6(1), 14-29.

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27–30. https://search.informit.org/doi/10.3316/informit.738299924514584

Bonn, M. A., Kim, W. G., & Cho, M. (2016). Purchasing Wine Online: The Effects of Social Influence, Perceived Usefulness, Perceived Ease of Use, and Wine Involvement. Journal of Hospitality Marketing & Management, 25. https://doi.org/10.1080/19368623.2016.1115382

Brown, S.A., Dennis, A.R. and Venkatesh, V. (2016), “Predicting collaboration technology use: integrating technology adoption and collaboration research”, Journal of Management Information Systems, Vol. 27 No. 2, pp. 9-53.

Casini, L., Contini, C., Romano, C., & Scozzafava, G. (2013). Trends in food consumptions: what is happening to generation X? British Food Journal, 117(2). BFJ-10-2013-0283

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.

Chong, A.Y.L. (2013), “Predicting m-commerce adoption determinants: a neural network approach”, Expert Systems with Applications, Vol. 40 No. 2, pp. 523-530.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Quarterly, 13(3), 319. 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. Global Scientific Journals, 7(2).

Debczak, M. (2023, March 10). Here Are the Age Ranges for Millennials, Gen Z, and Gen Alpha. Mental Floss. https://www.mentalfloss.com/article/609811/age-ranges-millennials-and-generation-z

Durai, A. (2023, January 10). 2023 food trends: Food delivery will continue to rise in Malaysia. The Star. https://www.thestar.com.my/lifestyle/living/2023/01/10/2023-food-trends-food-delivery-will-continue-to-rise-in-malaysia

Fernandez, H. (2012, November 1). 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

George, D., & Mallery, P. (2006). SPSS for Windows Step-by-Step: A simple guide and reference, 13.0 Update. Allyn & Bacon, Boston, MA.

Gore, S., Goldberg, A., Huang, M. H., Shoemaker, M. & Blackwood, J. (2019). Development and validation of a quality appraisal tool for validity studies (QAVALS). Physiotherapy Theory and Practice, 37(5), 646-654. https://doi.org/10.1080/09593985.2019.1636435

Groß, M. (2015), “Mobile shopping: a classification framework and literature review”, International Journal of Retail & Distribution Management, Vol. 43 No. 3, pp. 221-241.

Gunden, N., Morosan, C., & DeFranco, A. L. (2020). Consumers’ persuasion in online food delivery systems. Journal of Hospitality and Tourism Technology, 11(3). https://www.emerald.com/insight/content/doi/10.1108/JHTT-10-2019-0126/full/pdf?title=consumers-persuasion-in-online-food-delivery-systems

Hair, J., Hult, T. G. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM) (1st ed.). SAGE Publications, Inc.

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

Hong, W., Thong, J. Y., Chasalow, L. C., & Dhillon, G. (2011). User Acceptance of Agile Information Systems: A Model and Empirical Test. Journal of Management Information Systems, 28(1), 235–272. https://doi.org/10.2753/mis0742-1222280108

Hooi, R., Leong, T. K., & Yee, L. H. (2021). Intention to use online food delivery service in Malaysia among university students. CoMBInES- Conference on Management, Business, Innovation, Education and Social Sciences, 1(1), 60-73. https://journal.uib.ac.id/index.php/combines/article/view/4415/1140

Jun, K., Yoon, B., Lee, S., & Lee, D. S. (2021, December). Factors Influencing Customer Decisions to Use Online Food Delivery Service during the COVID-19 Pandemic. Effects and Implications of COVID-19 for the Human Senses, Consumer Preferences, Appetite and Eating Behaviour, 11(1), 64. https://doi.org/10.3390/foods11010064

Khalil, N. (2014). Factors affecting the consumer’s attitudes on online shopping in Saudi Arabia International Journal of Scientific and Research Publications, 4(11), 1-8. https://www.ijsrp.org/research-paper-1114/ijsrp-p3555.pdf

Kim, Y., Dykema, J., Stevenson, J. C., Black, P., & Moberg, D. P. (2018). Straightlining: Overview of Measurement, Comparison of Indicators, and Effects in Mail–Web Mixed-Mode Surveys. Social Science Computer Review, 37(2), 214–233. https://doi.org/10.1177/0894439317752406

Kirom, N. R., Sudarmiatin, S., & Hermawan, A. (2022). E-Commerce Strategy for MSME Innovation Development in the New Normal Era. International Journal of Environmental, Sustainability and Social Science, 3(1), 169–178. https://doi.org/10.38142/ijesss.v3i1.125

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press. López-Bonilla, L. M. & López-Bonilla, J. M. (2011). The role of attitudes in the TAM: a theoretically unnecessary construct? British Journal of Educational Technology, 42, 6, E160–E162.

Koiri, S.K., Mukherjee, S. and Dutta, S. (2019). A study on determining the factors impacting consumer perception regarding the online food delivery apps in Guwahati. GIS Business, 14(6).

Korstjens, I., & Moser, A. (2017). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. European Journal of General Practice, 24(1), 120–124. https://doi.org/10.1080/13814788.2017.1375092

Kussujaniyatun S., Sujatmika, Harilaksana D., & Hartati A.S. (2022). Analysis of the Socio-Economic Effect and Performance Expectancy on the Use of Financial Technology Applications. Russian Journal of Agricultural and Socio-Economic Sciences, 132(12), 68–72.

Lau, T., & Yat, D. N. C. (2019, June 27). (PDF) Online Food Delivery Services: Making Food Delivery the New Normal. ResearchGate. Retrieved July 17, 2023, from https://www.researchgate.net/profile/Teck-Chai-Lau-2/publication/334050513_Online_Food_Delivery_Services_Making_Food_Delivery_the_New_Normal/links/5d148043458515c11cfb6d5e/Online-Food-Delivery-Services-Making-Food-Delivery-the-New-Normal.pdf

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). https://doi.org/10.3390/su11113141

Li, C., Mirosa, M., & Bremer, P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability, 12(14), 5528.

Lim, S., Xue, L., Yen, C.C., Chang, L., Chan, H.C., Tai, B.C., Duh, H.B. and Choolani, M. (2011), “A study on Singaporean women’s acceptance of using mobile phones to seek health information”, International Journal of Medical Informatics, Vol. 80 No. 12, pp. 189-202.

Majid, U. (2018). Research fundamentals: study design, population, and sample size. Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal, 2(1), 1–7. https://doi.org/10.26685/urncst.16

Mirzaei, A., Carter, S. R., Patanwala, A. E., & Schneider, C. R. (2022). Missing data in surveys: Key concepts, approaches, and applications. Research in Social and Administrative Pharmacy, 18(2), 2308-2316.

Mishra, P., Pandey, C. K., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103/aca.aca_157_18

Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192

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

Nestle, M., Wing, R., Birch, L., DiSogra, L., Drewnowski, A., Middleton, S., Sigman-Grant, M., Sobal, J., Winston, M., & Economos, C. (1998). Behavioral and social influences on food choice. PubMed. https://pubmed.ncbi.nlm.nih.gov/9624880/

Nikolopoulou, K. (2022). What Is Convenience Sampling? | Definition & Examples. Scribbr. Retrieved October 9, 2022, from https://www.scribbr.com/methodology/convenience-sampling/

Parsons, T. (1963). On the Concept of Influence. Public Opinion Quarterly, 27(1), 37. https://doi.org/10.1086/267148

Pitchay, A. A., Ganesan, Y., Zulkifli, N. N., & Khaliq, A. (2021). 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

Poon, W. C., & Tung, S. E. H. (2022a). 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. https://doi.org/10.1108/EJMBE-04-2021-0128

Poon, W. C., & Tung, S. E. H. (2022b). Consumer risk perception of online food delivery during the COVID-19 Movement Control Order (MCO) in Malaysia. Journal of Foodservice Business Research. https://doi.org/10.1080/15378020.2022.2054657

Prasetyo, Y. T., Tanto, H., Mariyanto, M., Hanjaya, C., Young, M. N., Persada, S. F., Miraja, B. A., & Redi, A. A. (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. https://doi.org/10.3390/joitmc7010076

Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4), 3-13.

Ramesh, R., Venkatesa Prabhu, S., Sasikumar, B., Kiruthika Devi, B. S., Prasath, P., & Praveena Rachel Kamala, S. (2023). An empirical study of online food delivery services from applications perspective. Materials Today: Proceedings, 80, 1751–1755. https://doi.org/10.1016/j.matpr.2021.05.500

Ramos, K. (2021), Factors influencing customers' continuance usage intention of food delivery apps during COVID-19 quarantine in Mexico. British Food Journal, 124(3), 833-852. https://doi.org/10.1108/BFJ-01-2021-0020

Rasli, M. A., Zulkefli, N. H., Salleh, N. S. A., Ghani, F. A., Razali, N. A., & Idris, R. S. (2020). Determinants of Behavioural Intention on Online Food Delivery (OFD) APPS: Extending UTAUT2 with Information Quality. Global Business and Management Research: An International Journal. http://gbmrjournal.com/pdf/v12n4/V12N4-66.pdf

Rosa, H. R. D., & Separa, L. A. C. (2022). Satisfactions of Customers by Using Online Food Application Services During Covid-19 Pandemic. International Journal of Multidisciplinary: Applied Business and Education Research, 3(9), 1765-1776.

Schonlau, M., & Toepoel, V. (2015). Straightlining in Web survey panels over time. DOAJ (DOAJ: Directory of Open Access Journals). https://doi.org/10.18148/srm/2015.v9i2.6128

Shaikh, A.A., Glavee-Geo, R. and Karjaluoto, H. (2018), “How relevant are risk perceptions, effort, and performance expectancy in mobile banking adoption?”, International Journal of E-Business Research (IJEBR), Vol. 14 No. 14(2), pp. 39-60.

Statista. (2023, May 5). Internet penetration rate in Malaysia 2013-2028. https://www.statista.com/statistics/975058/internet-penetration-rate-in-malaysia/

Stuckey, H. L. (2015). The second step in data analysis: Coding qualitative research data. Journal of Social Health and Diabetes, 3(01), 007-010.

Su, C., & Chao, C. (2022). Investigating Factors Influencing Nurses’ Behavioral Intention to Use Mobile Learning: Using a Modified Unified Theory of Acceptance and Use of Technology Model. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.673350

Tan, H., & Kim, V. W. E. (2021). Examining the factors that Influence consumer satisfaction with online food delivery in Klang Valley, Malaysia. The Journal of Management Theory and Practice (JMTP), 2(2). 88-95. https://journal.unisza.edu.my/jmtp/index.php/jmtp/article/view/115/65

Thompson, R. W., Higgins, C. P., & Howell, J. M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. Management Information Systems Quarterly, 15(1), 125. https://doi.org/10.2307/249443

Venkatesh, V., Morris, M. A., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. Management Information Systems Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Watson, R. (2015). Quantitative research. Nursing Standard, 29(31), 44–48. https://doi.org/10.7748/ns.29.31.44.e8681

Wetzels, R., & Wagenmakers, E. J. (2012). A default Bayesian hypothesis test for correlations and partial correlations. Psychonomic bulletin & review, 19(6), 1057-1064.

Wright, K. B. (2017). Researching Internet-Based Populations: Advantages and Disadvantages of Online Survey Research, Online Questionnaire Authoring Software Packages, and Web Survey Services. Journal of Computer-Mediated Communication, 10(3). https://doi.org/10.1111/j.1083-6101.2005.tb00259.x

Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35, 150–162. https://doi.org/10.1016/j.jretconser.2016.12.013

Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal of Hospitality Management, 91, Article 102683.

DOI: http://dx.doi.org/10.47256/ijt.v2i2.368


  • There are currently no refbacks.