The current study investigated the moderating effect of age, gender and an experience in the relationship between behavioural intention to use and usage of online food delivery applications (OFDA) in Sarawak. The framework of this research was drawn from the perspective of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model. The study was based on a sample gathered from users of online food delivery applications in Sarawak. Data were collected using a self-administered online questionnaire. Of the 411 returned questionnaires, 400 questionnaires were valid for analysis. IBM-SPSS Amos 24.0 procedures were utilised to analyse the data and test the hypotheses. This study focused on the significance of all constructs of the proposed conceptual model, and new findings pertaining to these constructs have been highlighted. The findings of the study lead to the conclusion that age, gender, and experience acted as partial moderator in the relationship between behavioural intention to use and usage. The significance of the findings enable to highlight the important factors in influencing people’s behaviours on online food delivery applications among users in aforesaid context.
Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. International Journal of Information Management, 34(7), 1177–1190.
AlHadid, I., Abu-Taieh, E., Alkhawaldeh, R. S., Khwaldeh, S., Masa’deh, R., Kaabneh, K., & Alrowwad, A. (2022). Predictors for E-Government Adoption of SANAD App Services Integrating UTAUT, TPB, TAM, Trust, and Perceived Risk. IJERPH, 19(14), 1–26.
Pitchay, 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, 124(3), 732-753.
Alvi. (2016). Munich Personal RePEc Archive: A Manual for Selecting Sampling Techniques in Research. [Online] Available at: https://mpra.ub.uni-muenchen.de/70218/ [Assessed on 8 November 2022].
Belanche, D., Cenjor, I., & Pérez-Rueda, A. (2019). Instagram Stories versus Facebook Wall: an advertising effectiveness analysis. Spanish Journal of Marketing - ESIC, 23(1), 69–94.
Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020a). Mobile apps use and WOM in the food delivery sector: The role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability (Switzerland), 12(10).
Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020b). Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility. Sustainability, 12(10), 4275.
Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers and Education, 105, 1–13.
Chai, L. T., Ng, D., & Yat, C. (2019). Online Food Delivery Services: Making Food Delivery, the New Normal. Journal of Marketing Advance and Practices, 1(1), 62-77.
Cho, M., Bonn, M. A., & Li, J. (Justin). (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108–116.
Dirsehan, T., & Cankat, E. (2021). Role of mobile food-ordering applications in developing restaurants’ brand satisfaction and loyalty in the pandemic period. Journal of Retailing and Consumer Services, 62.
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: a flexible statistical power analysis program for the social, behavioural, and biomedical sciences. Behaviour Research Methods, 39(2), 175–191.
The Star. (n.d.). Food delivery will continue to be a big trend in 2020. [Online] Available at: https://www.thestar.com.my/food/food-news/2020/01/04/food-delivery-will-continue-to-be-a-big-trend-in-2020 [Assessed on February 24, 2024].
NST. (n.d.). Foodpanda records 100pct growth in 2017. (n.d.). [Online] Available at: https://www.nst.com.my/business/2018/06/381431/foodpanda-records-100pct-growth -2017. [Assessed on May 2, 2023].
Statista. (n.d.). Global online food delivery market size 2017-2028. [Online] Available at https://www.statista.com/statistics/1170631/online-food-delivery-market-size-worldwide/. [Assesed on February 24, 2024].
Statista. (n.d.). Global online food delivery market size 2027. [Online] Available at https://www.statista.com/statistics/1170631/online-food-delivery-market-size-worldwide/. [Assesed on March 21, 2023].
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.
Hair, Joseph 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.
Hassan, M. (2018). Effect of Rebranding on the Customer Satisfaction of Foodpanda Bangladesh Limited. BRAC Business School.
South China Morning Post. (n.d.). Hong Kong social distancing: malls, delivery platforms seek to turn restaurants’ loss into their gain. [Online]. Available at: https://www.scmp.com/news/hong-kong/hong-kong-economy/article/3162425/hong-kong-social-distancing-malls-delivery. [Assessed on February 24, 2024].
Hwang, J., & Kim, H. (2019). Consequences of a green image of drone food delivery services: The moderating role of gender and age. Business Strategy and the Environment, 28(5), 872–884.
Hwang, J., Lee, J. S., & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94–103.
Kang, H. (2021). Sample Size Determination And Power Analysis Using The G*Power Software. Journal of Educational Evaluation for Health Profession, 18, 17.
Kasilingam, D., & Krishna, R. (2021). Understanding the adoption and willingness to pay for internet of things services. International Journal of Consumer Studies, 46(1), 102–131.
Kaur, P., Dhir, A., Talwar, S., & Ghuman, K. (2020). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33(4), 1129–1159.
Kumar, S., Jain, A., & Hsieh, J. K. (2021). Impact of apps aesthetics on revisit intentions of food delivery apps: The mediating role of pleasure and arousal. Journal of Retailing and Consumer Services, 63.
Kumar, S., & Shah, A. (2021). Revisiting food delivery apps during COVID-19 pandemic? Investigating the role of emotions. Journal of Retailing and Consumer Services, 62.
Liébana-Cabanillas, F., Singh, N., Kalinic, Z., & Carvajal-Trujillo, E. (2021). Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach. Information Technology and Management, 22(2), 133-161.
Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users’ social networking site continuance: A gender difference perspective. Information & Management, 54(3), 383–395.
Nayan, N., & Hassan, M. K. A. (2020). Customer Satisfaction Evaluation for Online Food Service Delivery System in Malaysia. Journal of Information System and Technology Management, 5(19), 123-136.
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.
McKinsey. (n.d.). Ordering in: The rapid evolution of food delivery. [Online]. Available at https://www.mckinsey.com/industries/technology-media-and-telecommunications/ our-insights/ordering-in-the-rapid-evolution-of-food-delivery [Assessed on February 24, 2024].
Pandey, S., Chawla, D., & Puri, S. (2022). Food delivery apps (FDAs) in Asia: an exploratory study across India and the Philippines. British Food Journal, 124(3), 657–678.
Paul, C., & Spiru, L. (n.d.). From Age to Age: Key Gerontographics Contributions to Technology Adoption by Older Adults.
Petrov?i?, A., Rogelj, A., & Dolni?ar, V. (2018). Smart but not adapted enough: Heuristic evaluation of smartphone launchers with an adapted interface and assistive technologies for older adults. Computers in Human Behavior, 79, 123–136.
Poon, W. C., & Tung, Hui En, S. (2022). The rise of online food delivery culture during the COVID-19 pandemic?: an analysis of intention and its associated risk culture. European Journal of Management and Business Economics, 0(0), 00–00.
Ray, A., Dhir, A., Bala, P. K., & Kaur, P. (2019). Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. Journal of Retailing and Consumer Services, 51, 221–230.
Rosman, M., Yapp, E. H. T., & Kataraian, S. (2022). Key Determinants of Continuance Usage Intention: An Empirical Study of Mobile Food Delivery Apps among Malaysians. Proceedings 2022, 82(1), 15.
Saad, A. T. (2021). Factors affecting online food delivery service in Bangladesh: an empirical study. British Food Journal, 123(2), 535–550.
Saunders, M., Lewis, P., & Thornhill, A. (2019). Understanding Reseach Philisophy and Approaches to Theory Development. Research Methods for Business Students, 8th ed., United Kingdom: Pearson.
Shankar, A., Jebarajakirthy, C., Nayal, P., Maseeh, H. I., Kumar, A., & Sivapalan, A. (2022). Online food delivery: A systematic synthesis of literature and a framework development. International Journal of Hospitality Management, 104.
Shroff, A., Shah, B. J., & Gajjar, H. (2022). Online food delivery research: a systematic literature review. International Journal of Contemporary Hospitality Management, 34(8), 2852–2883.
Singh, S., Singh, N., Kalini?, Z., & Liébana-Cabanillas, F. J. (2021). Assessing determinants influencing continued use of live streaming services: An extended perceived value theory of streaming addiction. Expert Systems with Applications, 168.
Siva, M., Nayak, D. P., & Narayan, K. A. (n.d.). Strengths and weaknesses of online surveys. IOSR Journal of Humanities and Social Sciences, 24(5).
Song, H. J., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95.
MCMC. (n.d.). Suruhanjaya Komunikasi Dan Multimedia Malaysia Malaysian Communications and Multimedia Commission Internet Users Survey 2020. [Online] Available at: http://www.mcmc.gov.my. [Assesed on 20 December 2021].
Surya, A. P., Sukresna, I. M., & Mardiyono, A. (2021). Factors affecting intention to use food order-delivery feature of ride-hailing applications: The UTAUT approach. International Journal of Business and Society, 22(3), 1363–1383.
Talukder, M., Aroos-Sheriffdeen, S., Khan, M. I., Quazi, A., & Abdullah, A. B. M. (2023). Usage behavior of mHealth service users in Australia: do user demographics matter? Journal of Services Marketing, 37(7), 801–816.
Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Why do people purchase from online travel agencies (OTAs)? A consumption values perspective. International Journal of Hospitality Management, 88, 102534.
Talwar, S., Dhir, A., Kaur, P., Zafar, N., & Alrasheedy, M. (2019). Why Do People Share Fake News? Associations between the Dark Side of Social Media Use and Fake News Sharing Behaviour. Journal of Retailing and Consumer Services, 51, 72–82.
Tan, S. Y., Lim, S. Y., & Yeo, S. F. (2021). Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic. F1000Research 2021 10:972, 10, 972.
Tandon, A., Dhir, A., Kaur, P., Kushwah, S., & Salo, J. (2020a). Behavioural reasoning perspectives on organic food purchase. Appetite, 154.
Tandon, A., Dhir, A., Kaur, P., Kushwah, S., & Salo, J. (2020b). Why do people buy organic food? The moderating role of environmental concerns and trust. Journal of Retailing and Consumer Services, 57.
Tandon, A., Jabeen, F., Talwar, S., Sakashita, M., & Dhir, A. (2021). Facilitators and inhibitors of organic food buying behavior. Food Quality and Preference, 88, 104077.
Tandon, A., Kaur, P., Bhatt, Y., Mäntymäki, M., & Dhir, A. (2021). Why do people purchase from food delivery apps? A consumer value perspective. Journal of Retailing and Consumer Services, 63.
Terblanche, N., & Kidd, M. (2022). Adoption Factors and Moderating Effects of Age and Gender That Influence the Intention to Use a Non-Directive Reflective Coaching Chatbot. SAGE Open, 12(2).
Uttley, J. (2019). Power Analysis, Sample Size, and Assessment of Statistical Assumptions—Improving the Evidential Value of Lighting Research. The Journal of the Illuminating Engineering Society, 15(2–3), 143–162.
Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly: Management Information Systems, 36(1), 157-178.
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.
Wu, M. J., Zhao, K., & Fils-Aime, F. (2022). Response rates of online surveys in published research: A meta-analysis. Computers in Human Behaviour Reports, 7, 100206.
Yeo, S. F., Tan, C. L., Teo, S. L., & Tan, K. H. (2021). The role of food apps servitization on repurchase intention: A study of FoodPanda. International Journal of Production Economics, 234.
Yi, Y., & Chiu, D. K. W. (2023). Public information needs during the COVID-19 outbreak: a qualitative study in mainland China. Library Hi Tech, 41(1), 248–274.
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, 102683.
Gadiman, N. S. B., Mohamed, N. N., & Jaafar, N. (2024). Investigating the Moderating Effect of Age, Gender, and an Experience in the Relationship between Behavioural Intention to Use and Usage of Online Food Delivery Applications (OFDA) in Sarawak. International Journal of Academic Research in Business and Social Sciences, 14(10), 2534–2550.
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