Home / Regular Issue / JSSH Vol. 30 (3) Sep. 2022 / JSSH-8435-2021


Determinants of Consumers’ Purchase Behaviour Towards Online Food Delivery Ordering (OFDO)

Sylvia Nabila Azwa Ambad, Hazliza Haron and Nor Irvoni Mohd Ishar

Pertanika Journal of Social Science and Humanities, Volume 30, Issue 3, September 2022

DOI: https://doi.org/10.47836/pjssh.30.3.08

Keywords: Consumer adoption, consumer behaviour, food safety consciousness, online food delivery ordering, perceived benefit, positive online comments, reference groups

Published on: 6 September 2022

Nowadays, customers globally are turning to online shopping for almost everything, which is considered a new norm expected to remain indefinitely. Although online food delivery ,has become a trend, several issues hinder customers from purchasing food online, such as poor customer reviews, trust issues, low food quality, poor packaging, delay in delivery, and risk associated with personal data. Thus, this study aims to identify the effect of reference groups, positive online comments, perceived risks, perceived benefits, and food safety consciousness of online food delivery ordering (OFDO) adoption. The convenience sampling technique was used to collect data from Malaysian consumers. The questionnaire survey data was collected from 288 respondents using the structural equation modelling-partial least squares (SEM-PLS) method. This study shows that reference groups, positive online comments, perceived benefits, and food safety consciousness positively affect the purchase behaviour of online food delivery services. Among all factors, the perceived benefit of online food delivery ordering (OFDO) has the largest effect on consumer behaviour (f2=0.273). Customers prefer using OFDO due to the application’s user-friendly interface, variety of choices, ease of ordering from anywhere and anytime, better discounts, rewards, and cashback.

  • 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

  • Ali, S., Khalid, N., Javed, H. M. U., & Islam, D. M. (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), 10. https://doi.org/10.3390/joitmc7010010

  • Arif, I., Aslam, W., & Siddiqui, H. (2020). Influence of brand related user-generated content through Facebook on consumer behaviour: A stimulus-organism-response framework. International Journal of Electronic Business, 15(2), 109-132. https://doi.org/10.1504/ijeb.2020.106502

  • Bauer, R. A. (1960) Consumer behavior as risk taking. In D. F. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 23-33). Harvard University Press.

  • Bearden, W. O., & Etzel, M. J. (1991). Reference group influence on product and brand purchase decisions. In H. H. Kassarjian, & T. S. Robertson (Eds.), Perspectives in consumer behaviour (pp. 435-451). Prentice-Hall.

  • Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139-168. https://doi.org/10.1086/209154

  • Cain, M. K., Zhang, Z., & Yuan, K.-H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716-1735. https://doi.org/10.3758/s13428-016-0814-1

  • 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.

  • Choi, J., Lee, A., & Ok, C. (2013). The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: A study of street food. Journal of Travel & Tourism Marketing, 30(3), 222-237. https://doi.org/10.1080/10548408.2013.774916

  • Dieffenbach, M. C., Gillespie, G. S., Burns, S. M., McCulloh, I. A., Ames, D. L., Dagher, M. M., Falk, E. B., & Lieberman, M. D. (2020). Neural reference groups: A synchrony-based classification approach for predicting attitudes using fNIRS. Social Cognitive and Affective Neuroscience, 16(1-2), 117-128. https://doi.org/10.1093/scan/nsaa115

  • Ding, S., Lin, J., & Zhang, Z. (2020). Influences of reference group on users’ purchase intentions in network communities: From the perspective of trial purchase and upgrade purchase. Sustainability, 12(24), 10619. https://doi:10.3390/su122410619

  • Dsouza, D., & Sharma, D. (2020). Online food delivery portals during COVID-19 times: An analysis of changing consumer behavior and expectations. International Journal of Innovation Science, 13(2), 218-232. https://doi.org/10.1108/IJIS-10-2020-0184

  • Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., Kumar, V., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168

  • Franke, G., & Sarstedt, M (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430-447. https://doi.org/10.1108/IntR-12-2017-0515

  • Gassler, B., Fronzeck, C., & Spiller, A. (2019). Tasting organic: The influence of taste and quality perception on the willingness to pay for organic wine. International Journal of Wine Business Research, 31(2), 221-242. https://doi.org/10.1108/IJWBR

  • GlobalData UK Ltd. (2020, Sept 8). COVID-19 accelerates e-commerce growth in Malaysia. https://www.globaldata.com/covid-19-accelerates-e-commerce-growth-malaysia-says-globaldata/

  • Gómez, J., Orcos, R., & Volberda, H. W. (2021). How imitation of multiple reference groups drives the evolution of firm strategy. Review of Managerial Science, 1-32. https://doi.org/10.1007/s11846-020-00422-z

  • Gupta, V., & Duggal, S. (2021). How the consumer’s attitude and behavioural intentions are influenced: A case of online food delivery applications in India. International Journal of Culture, Tourism and Hospitality Research, 15(1), 77-93. https://doi.org/10.1108/IJCTHR-01-2020-0013

  • Gupta, V., & Sajnani, M. (2020). Risk and benefit perceptions related to wine consumption and how it influences consumers’ attitude and behavioural intentions in India. British Food Journal, 122(8), 2569-2585. https://doi.org/10.1108/BFJ-06-2019-0464

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt. M. (2014). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage.

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage.

  • Hair, J. F., Jr., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning: International Journal of Strategic Management, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001

  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

  • Henseler, J., Ringle, C., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8

  • Higgs, S., & Thomas, J. (2016). Social influences on eating. Current Opinion in Behavioral Sciences, 9, 1-6. https://doi.org/10.1016/j.cobeha.2015.10.005

  • Hoyer, W. D., MacInnis, D. J., & Pieters, R. (2001). Customer behavior. Houghton Mifflin.

  • Hsu, C. H., Kang, S. K., & Lam, T. (2006). Reference group influences among Chinese travelers. Journal of Travel Research, 44(4), 474-484. https://doi.org/10.1177/0047287505282951

  • Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a reliability coefficient be too high? Journal of Consumer Psychology, 10(1), 55-58. https://doi.org/10.2307/1480474

  • Jin, S. V., & Phua, J. (2015). Making reservations online: The impact of consumer-written and system-aggregated user-generated content (UGC) in travel booking websites on consumers’ behavioral intentions. Journal of Travel & Tourism Marketing, 33(1), 101-117. https://doi.org/10.1080/10548408.2015.1038419

  • Johnston, K. L., & White, K. M. (2003). Binge-drinking: A test of the role of group norms in the theory of planned behaviour. Psychology and Health, 18(1), 63-77. https://doi.org/10.1080/0887044021000037835

  • Kaur, S., & Arora, S. (2021). Role of perceived risk in online banking and its impact on behavioral intention: Trust as a moderator. Journal of Asia Business Studies, 15(1), 1-30. https://doi.org/10.1108/JABS-08-2019-0252

  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making mod in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.

  • Kimes, S. E. (2011). The current state of online food ordering in the US restaurant industry. Cornell Hospitality Report, 11(17), 6-18.

  • Lavrakas, P. J. (2008). Encyclopedia of survey research methods (Vol. 1). Sage Publications, Inc. https://dx.doi.org/10.4135/9781412963947.n174

  • Lee, J., & Jin, C. (2019). The relationship between self-concepts and flaming behavior: Polarity of the online comments. Journal of Theoretical and Applied Information Technology, 97(19), 2518-2529. https://doi.org/1992-8645

  • Leung, X. Y., & Cai, R. (2021). How pandemic severity moderates digital food ordering risks during COVID-19: An application of prospect theory and risk perception framework. Journal of Hospitality and Tourism Management, 47, 497-505. https://doi.org/10.1016/j.jhtm.2021.05.002

  • Li, C., Mirosa, M., & Bremer, P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability, 12(14), 5528. https://doi.org/10.3390/su12145528

  • Mehrolia, S., Alagarsamy, S., & Solaikutty, V. M. (2021). Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. International Journal of Consumer Studies, 45(3), 396-408. https://doi.org/10.1111/ijcs.12630

  • Moons, I., & De Pelsmacker, P. (2015). An extended decomposed theory of planned behaviour to predict the usage intention of the electric car: A multi-group comparison. Sustainability, 7(5), 6212-6245. https://doi.org/10.3390/su7056212

  • Park, D. H., Lee, J., & Han, I. (2007). The effect of online consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148. https://doi.org/ 10.2753/jec1086-4415110405

  • Pitchay, A. A., Ganesan, Y., Zulkifli, N. S., & Khaliq, A. (2021). Determinants of customers’ intention to use online food delivery application through smartphone in Malaysia. British Food Journal, 24(3), 732-753. https://doi.org/10.1108/BFJ

  • Prasetyo, Y. T., Tanto, H., Mariyanto, M., Hanjaya, C., Young, M. N., Persada, S. F., Miraja, B. A., & 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

  • Punj, G. (2012). Income effects on relative importance of two online purchase goals: Saving time versus saving money? Journal of Business Research, 65(5), 634-640. https://doi.org/10.1016/j.jbusres.2011.03.003

  • Raman, P., & Aashish, K. (2021). To continue or not to continue: A structural analysis of antecedents of mobile payment systems in India. International Journal of Bank Marketing, 39(2), 242-271. https://doi.org/10.1108/IJBM-04-2020-0167

  • Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An updated guide and practical guide to statistical analysis (2nd ed.). Pearson.

  • Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial Least Squares Structural Equation Modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617-1643. https://doi.org/10.1080/09585192.2017.1416655

  • Sa’ait, N., Kanyan, A., & Nazrin, M. F (2016). The effect of e-WOM on customer purchase intention. International Academic Research Journal of Social Science, 2(1), 73-80.

  • Scott, S. (2021). Using policy to improve patient outcomes: The role of a National Clinical Reference Group clinical member. Orthopaedics and Trauma, 35(2), 90-95. https://doi.org/10.1016/j.mporth.2021.01.005

  • See-Kwong, G, S.-R. N., Shiun-Yi W, & C, L. (2017). Outsourcing to online food delivery services: Perspective of F&B business owners. The Journal of Internet Banking and Commerce, 22(2), 1-18.

  • Sethna, B. N., Hazari, S., & Bergiel, B. (2017). Influence of user generated content in online shopping: Impact of gender on purchase behaviour, trust, and intention to purchase. International Journal of Electronic Marketing and Retailing, 8(4), 344-371. https:// doi.org/10.1504/IJEMR.2017.087719

  • Shafiee, S. N. Z., & Wahab, M. R. A. (2021). Consumer attitude, satisfaction, food safety awareness, and purchase intention of food ordered through online food delivery using mobile application in Penang Island, Malaysia. Malaysian Applied Biology, 50(2), 165-175. https://doi.org/10.55230/mabjournal.v50i2.2161

  • Sinha, P., & Singh, S. (2014). Determinants of consumers’ perceived risk in online shopping: A study. Indian Journal of Marketing, 44(1), 22-32. https://doi.org/10.17010/ijom/2014/v44/i1/80468

  • Siyal, M., Siyal, S., Wu, J., Pal, D., & Memon, M. M. (2021). Consumer perceptions of factors affecting online shopping behavior: An empirical evidence from foreign students in China. Journal of Electronic Commerce in Organizations, 19(2), 1-16. http://doi.org/10.4018/JECO.2021040101

  • Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310-1323. https://doi.org/10.1016/j.tourman.2010.12.011

  • Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 94-109). Brooks-Cole.

  • Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36, 198-213. https://doi.org/10.1016/j.tourman.2010.12.011

  • Verlegh, P. W., & Candel, M. J. (1999). The consumption of convenience foods: Reference groups and eating situations. Food Quality and Preference, 10(6), 457-464. https://doi.org/10.1016/S0950-3293(99)00042-7

  • Vesoulis, A. (2021, December 28). “Profit doesn’t exist anymore”. Restaurants that barely survived COVID-19 closures now face labor, inflation and supply chain crises. Time. https://time.com/6129713/restaurants-closing-covid-19/

  • 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

  • Wei, M. F., Luh, Y. H., Huang, Y. H., & Chang, Y. C. (2021). Young generation’s mobile payment adoption behavior: Analysis based on an extended UTAUT model. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 618-637. https://doi.org/10.3390/jtaer16040037.

  • Xiao, Z., Zhang, J., Li, D., & Chen, C. (2015). Trust in online food purchase behavior: An exploration in food safety problem for produce e-retailers. Advance Journal of Food Science and Technology, 8(10), 751-757. https://doi.org/10.19026/ajfst.8.1602

  • Yadav, R., & Pathak, G. S. (2017). Determinants of consumers’ green purchase behavior in a developing nation: Applying and extending the Theory of Planned Behavior. Ecological Economics, 134, 114-122. https://doi.org/10.1016/j.ecolecon.2016.12.019

  • Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253-269. https://doi.org/10.1108/IMDS-08-2014-0243

  • 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(c), 150-162. https://doi.org/10.1016/j.jretconser.2016.12.013

  • Zhang, M., Ding, S., & Bian, Y. (2017). The online reviews’ effects on internet consumer behavior. Journal of Electronic Commerce in Organizations, 15(4), 83-94. https://doi.org/10.4018/jeco.2017100107

  • Zhao, X., Deng, S., & Zhou, Y. (2017). The impact of reference effects on online purchase intention of agricultural products. Internet Research, 27(2), 233-255. https://doi.org/10.1108/IntR-03-2016-0082

ISSN 0128-7702

e-ISSN 2231-8534

Article ID


Download Full Article PDF

Share this article

Recent Articles