Decision-Making Food Delivery Application Technology in Bangkok and Metropolitan Region
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Abstract
The purposes of this research were to identify factors affecting technology acceptance of Food Delivery applications in Bangkok and the Metropolitan Region and to identify factors affecting the decision-making of a payment channel for ordering food online via the Food Delivery application. The Conceptual framework was based on the Technology Acceptance Model and Network Externalities. The quantitative research with a stratified random sampling technique was applied to select 830 application users to answer the self-reported questionnaire. The composition of the sample was divided between age and user experienced. The data were analyzed by Structure Equations Model (SEM) and Logistic regression model. The research found that the influencing major factors of the usage behaviors of the applications with the statistical significance included perceived number of peers, perceived complement, perceived ease of use, perceived usefulness, attitude towards technology, perceived security, descriptive norm, injunctive norm, subjective norm, behavior intention, and usage behavior, and factors affecting the decision-making a payment channel for food delivery application. The users choose a payment channel Mobile banking was the most, followed by cash, credit card, and bank account system, respectively. The results implied that the technology acceptance in using the application depended on the perception of the numbers of peers and perceived Complement.
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กัลยา วานิชย์บัญชา. (2554). สถิติสำหรับงานวิจัย. (พิมพ์ครั้งที่ 6). กรุงเทพมหานคร: ธรรมสาร.
นุชนาฏ สุทธิวงษ์. (2561). ปัจจัยที่มีอิทธิพลต่อการตั้งใจเลือกใช้บริการการชำระเงินผ่านแอพพลิเคชั่นเงินอิเล็กทรอนิกส์ (e-Money) ในกรุงเทพมหานคร. หลักสูตรบริหารธุรกิจมหาบัณฑิต. บัณฑิตวิทยาลัย: มหาวิทยาลัยสยาม.
ณัธภัชร เฉลิมแดน. (2563). พฤติกรรมผู้บริโภคในการสั่งอาหารแบบเดลิเวอรี่ผ่านโมบายแอปพลิเคชันช่วงเกิดโรคติดเชื้อไวรัสโคโรนาสายพันธุ์ใหม่ 2019 (COVID-19) ในเขตกรุงเทพมหานคร. วารสารบริหารธุรกิจอุตสาหกรรม. 2 (1), 92-106.
บุษรา ประกอบธรรม. (2556). การศึกษาการยอมรับเครือข่ายสังคมออนไลน์ของนักศึกษา:กรณีศึกษามหาวิทยาลัยกรุงเทพ. สุทธิปริทัศน์. 27 (81), 93-108.
ศูนย์วิจัยกสิกรไทย. (2564). ธุรกิจ Food Delivery ปี 64 มูลค่ารวมทะลัก5.58 หมื่นล้านบาทโต 24.4%. ออนไลน์. สืบค้นเมื่อ 27 สิงหาคม 2564. แหล่งที่มา: https://kasikornresearch.com/th/ analysis/k-econ/business/Pages/Covid-Travel-z3255.aspx.
Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
Berry, L.L., Kathleen S., and Dhruv G.., (2002). Understanding Service Convenience. Journal of Marketing, 66 (July), 1–17.
Bajaj, K. & Mehendale, S. (2016). Food - Delivery Start-Ups: In Search of the Core. Praband han: Indian Journal of Management, 9 (10). 42-53.
Chen, N.-H.; Hung, Y.-W. (2015). Online shopping orientation and purchase behavior for high-touch products. Int. J. Electron. Commer. Stud., 6, 187–202.
Chai, L.T., Yat, D.N.C. (2019). Online Food Delivery Services: Making Food Delivery the New Normal. J. Mark. Adv. Pract. 1, 62–77
Cochran, W.G. (1977). Sampling Techniques. (3rded.). New York: John Wiley & Sons, Inc.
Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of In formation Technology, MIS Quarterly. 13 (3), 319-339.
Farquhar, J. D., & Rowley, J. (2009). Convenience A Services Perspective. Marketing Theory, 9, 425-438.
Fishbein and Ajzen. 1975. Belief Attitude Intention and Behavior: An Introduction to theory and Research. Mass: Addison-Wesley.
Farrel and Klemperer (2007). Coordination and Lock-In: Competition with Switching Costs and Network Effects. Handbook of Industrial Organization, 3, 1967-2072.
Green and Kreuter. 1991. Health Promotion Planning : An Education and Environment Approach. 2 nd ed. Toronto: May Field Publishing Company.
Jiang, L. (Alice), Yang, Z. & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. J. Serv. Manag. Emerald 24 (2), 191–214.
Jahangir, N., & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking, African Journal of Business Management, 2 (1), 32-40.
Kapoor, A.P.; Vij, M. (2018). Technology at the dinner table: Ordering food online through mobile apps. J. Retail. Consum. Serv. 43, 342–351.
Katawetawaraks, C., & Wang, C. L. (2011). Online shopper behavior: influences of online shopping decision, Asian Journal of Business Research, 1 (2), 66-74.
Ngai, E.W.(2007). Gunasekaran, A. A review for mobile commerce research and applications. Decis. Support Syst, 43, 3–15.
Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social
network games: An application of extended technology acceptance model. Telematics and Informatics, 31 (1), 3–15.
Phoranee L., Veera B., (2018). Elucidating the Behavior of Consumers toward Online Grocery Shopping: The Role of Shopping Orientation. Journal of Internet Commerce, 17(4), 418-445.
Prabowo, G.T. and Nugroho, A. (2019). Factors that influence the attitude and behavioral intention of Indonesian users toward online food delivery service by the Go-Food application, Proceedings of the 12th International Conference on Business and Management Research (ICBMR 2018). 204-210.
Roca, J.C., Garci, J.J., & Vega, J.J. (2009). The importance of perceived trust, security and privacy in online trading system. Journal of Information Management & Computer Security, 17 (2), 96–113.
Rogers, E.M. (1983). Diffusion of Innovations; Free Press: New York, NY, USA.
Thi Thu H.N., Ninh N., Thi Bich L.N., Thi Thu H.P., Lan P. B., Hee C.M., (2019). Investigating Consumer Attitude and Intention towards Online Food Purchasing in an Emerging Economy: An Extended TAM Approach, MDPI Journal, 8(11), 576.
Yu, E., Hong, A., & Hwang, J. (2016). A socio-technical analysis of factors affecting the adoption of smart TV in Korea. Computers in Human Behavior, 61, 89–102.
