Factors affecting the intention to adopt food delivery apps: Value-based adoption model framework


Authors

  • Nguyen Thanh Nhan Office of the People’s Council and People’s Committee of Ben Luc District
  • Nguyen Thi Bich Phuong University of Economics Ho Chi Minh City
DOI: https://doi.org/10.57110/vnujeb.v3i4.190

Keywords:

Online food, intention, food delivery apps (FDA), VAM, food delivery

Abstract

This research investigates the factors affecting the intention to adopt food delivery apps in Ho Chi Minh City based on the Value-based adoption Model (VAM). The study was conducted using a structural equation model (SEM) to examine data collected from 344 responders. The research results show that benefit values including convenience and perceived enjoyment have a positive impact on perceived value. Sacrifice values include perceived complexity and perceived cost. Perceived value is negatively impacted by both perceived cost and perceived complexity. Perceived value has a strong and positive impact on the intention to adopt food delivery apps. Furthermore, the study results also indicate that perceived privacy risk negatively affects intention. This is one of the first studies applying VAM to investigate factors affecting consumer behavior in the context of Ho Chi Minh City.

References

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

Bianchi, C., & Andrews, L. (2012). Risk, trust, and consumer online purchasing behavior: A Chilean perspective. International Marketing Review. https://doi.org/10.1108/02651331211229750

Brown, L. G. (1990). Convenience in services marketing. Journal of Services Marketing, 4(1), 53-59. https://doi.org/10.1108/EUM0000000002505

Cho, M., Bonn, M. A., & Li, J. J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108-116. https://doi.org/10.1016/j.ijhm.2018.06.019

Choe, J. Y., Kim, J. J., & Hwang, J. (2021). Innovative marketing strategies for the successful construction of drone food delivery services: Merging TAM with TPB. Journal of Travel & Tourism Marketing, 38(1), 16-30. https://doi.org/10.1080/10548408.2020.1862023

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.1016/S0378-7206(01)00143-4

Deci, E. L. (1971). Effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18(1), 105. https://doi.org/10.1037/h0030644

Department of e-commerce and digital economy (2022). Vietnam e-commerce report in 2022. https://idea.gov.vn/?page=document

Dhir, A., Tandon, A., Kaur, P., & Bhatt, Y. (2021). Why do people purchase from food delivery apps? A consumer value perspective. Journal of Retailing and Consumer Services, 63, 102667-102667. https://doi.org/10.1016/j.jretconser.2021.102667

e-Conomy SEA. (2021). e-Conomy SEA 2021 report. https://www.bain.com/globalassets/noindex/2021/e_conomy_sea_2021_report.pdf

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Hair, J., Black, W., Babin, B., & Anderson, R. (2014). Multivariate data analysis (7th ed.). Pearson Education.

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

Hong, C., Choi, H., Choi, E. K., & Joung, H. W. (2021). Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. Journal of Hospitality and Tourism Management, 48, 509-518. https://doi.org/10.1016/j.jhtm.2021.08.012

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Jun, J., Cho, I., & Park, H. (2018). Factors influencing continued use of mobile easy payment service: an empirical investigation. Total Quality Management & Business Excellence, 29(9-10), 1043-1057. https://doi.org/10.1080/14783363.2018.1486550

Ariffin, S. K., Mohan, T., & Goh, Y. N. (2018). Influence of consumers’ perceived risk on consumers’ online purchase intention. Journal of Research in Interactive Marketing, 12(3), 309-327. https://doi.org/10.1108/jrim-11-2017-0100

Kapoor, A. P., & Vij, M. (2018). Technology at the dinner table: Ordering food online through mobile apps. Journal of Retailing and Consumer Services, 43, 342-351. https://doi.org/10.1016/j.jretconser.2018.04.001

Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126. https://doi.org/10.1016/j.dss.2005.05.009

Li, H., Wu, J., Gao, Y., & Shi, Y. (2016). Examining individuals' adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88, 8-17. https://doi.org/10.1016/j.ijmedinf.2015.12.010

Liu, F., Zhao, X., Chau, P. Y., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research, 25(3), 471-495. https://doi.org/10.1108/IntR-02-2014-0053

Nguyet, N. T. M., Viet, N. H., & Duong, V. T. (2022). The behavior of using online food delivery services during COVID-19 pandemic. Journal of Asian Business and Economic Studies - JABES, 32(9), 22-41. https://jabes.ueh.edu.vn/Content/ArticleFiles/b3cb7d0a-2f23-45db-a891-03bd24bb8786/JABES-2021-8-V289.pdf

Nikou, S. (2019). Factors driving the adoption of smart home technology: An empirical assessment. Telematics and Informatics, 45, 101283. https://doi.org/10.1016/j.tele.2019.101283

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879

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. https://doi.org/10.1016/j.jretconser.2019.05.025

Rogers, E. M. (2010). Diffusion of Innovations (4th ed.). Simon and Schuster.

Shankar, A., Jebarajakirthy, C., Nayal, P., Maseeh, H. I., Kumar, A., & Sivapalan, A. (2022). Online food delivery: A systematic synthesis of literature and framework development. International Journal of Hospitality Management, 104, 103240. https://doi.org/10.1016/j.ijhm.2022.103240

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. https://doi.org/10.1108/IJCHM-10-2021-1273

Song, H., Jasmine, W., Jung, Y., & Jeon, 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, 102943-102943. https://doi.org/10.1016/j.ijhm.2021.102943

Thaichon, P., Lobo, A., & Mitsis, A. (2014). Achieving customer loyalty through service excellence in internet industry. International Journal of Quality and Service Sciences, 6(4), 274-289. https://doi.org/10.1108/IJQSS-03-2014-0024

Thao, H. T. P., & Long, L. Q. (2021). The factors affect consumer’s trust and continuous usage intention of food delivery mobile app. Journal of Science Ho Chi Minh City Open University-Economic and Business Administration, 16(2), 99-116. https://doi.org/10.46223/hcmcoujs.econ.vi.16.2.931.2021

Thuy, L. N., Tho, T. A., Siem, T. T., & Dat, N. T. (2021). E-satisfaction and continuance intention to use mobile food ordering applications: A case study in Ho Chi Minh City. Journal of Science and Technology, 25-32.

Troise, C., O'Driscoll, A., Tani, M., & Prisco, A. (2021). Online food delivery services and behavioral intention - A test of an integrated TAM and TPB framework. British Food Journal, 123(2), 664-683. https://doi.org/10.1108/bfj-05-2020-0418

Vishwakarma, P., Mukherjee, S., & Datta, B. (2020). Travelers’ intention to adopt virtual reality: A consumer value perspective. Journal of Destination Marketing & Management, 17, 100456. https://doi.org/10.1016/j.jdmm.2020.100456

Wang, E. S. T., & Lin, R. L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behavior & Information Technology, 36(1), 2-10. https://doi.org/10.1080/0144929X.2016.1143033

Wang, H. Y., Liao, C., & Yang, L. H. (2013). What affects mobile application use? The roles of consumption values. International Journal of Marketing Studies, 5(2), 11. https://doi.org/10.5539/ijms.v5n2p11

Wang, H. Y., & Wang, S. H. (2010). Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management, 29(4), 598-608. https://doi.org/10.1016/j.ijhm.2009.11.001

Wang, Y. Y., Lin, H. H., Wang, Y. S., Shih, Y. W., & Wang, S. T. (2018). What drives users’ intentions to purchase a GPS navigation app: The moderating role of perceived availability of free substitutes. Internet Research, 28(1), 251-274. https://doi.org/10.1108/IntR-11-2016-0348

Zanetta, L. D. A., Hakim, M. P., & Gastaldi, G. B. (2021). The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Research International, 149, 110671. https://doi.org/https://doi.org/10.1016/j.foodres.2021.110671

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22. https://doi.org/10.2307/1251446

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. https://doi.org/10.1016/j.ijhm.2020.102683

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Published

25-08-2023

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How to Cite

Nguyen Thanh Nhan, & Nguyen Thi Bich Phuong. (2023). Factors affecting the intention to adopt food delivery apps: Value-based adoption model framework. VNU University of Economics and Business, 3(4), 77. https://doi.org/10.57110/vnujeb.v3i4.190

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