ANALISIS FAKTOR-FAKTOR YANG MEMENGARUHI NIAT PELANGGAN MENGGUNAKAN MOBILE FOOD APPS DI JAKARTA SELAMA PANDEMI COVID-19

Yodi Adiyoso

Abstract


A B S T R A K

Pandemi COVID-19 telah mengubah pola konsumsi masyarakat, meningkatkan ketergantungan pada layanan pengiriman makanan daring (Online Food Delivery/OFD) di Jakarta, seperti GoFood, GrabFood, dan ShopeeFood. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi niat pelanggan untuk menggunakan aplikasi OFD selama pandemi, dengan fokus pada variabel Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Trust (TR), Price-Saving Benefits (PSB), Time-Saving Benefits (TSB), Food Safety Risk Perception (FSRP), Perceived Severity (PS), dan Perceived Vulnerability (PV). Menggunakan pendekatan kuantitatif, data dikumpulkan dari 150 responden pengguna aplikasi OFD di Jakarta melalui kuesioner daring. Analisis data dilakukan dengan metode Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa PU, PSB, dan TSB memiliki pengaruh positif signifikan terhadap niat pelanggan, sedangkan PEOU, TR, FSRP, PS, dan PV tidak menunjukkan pengaruh signifikan. Temuan ini menyoroti pentingnya manfaat praktis dan efisiensi dalam mendorong adopsi OFD selama krisis kesehatan. Penelitian ini memberikan wawasan bagi pelaku bisnis OFD untuk meningkatkan strategi pemasaran dan kualitas layanan, sekaligus berkontribusi pada literatur perilaku konsumen di era pandemi.

A B S T R A C T

The COVID-19 pandemic has changed people's consumption patterns, increasing their dependence on online food delivery (OFD) services in Jakarta, such as GoFood, GrabFood, and ShopeeFood. This study aims to analyze the factors influencing customers' intentions to use OFD apps during the pandemic, focusing on the variables Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Trust (TR), Price-Saving Benefits (PSB), Time-Saving Benefits (TSB), Food Safety Risk Perception (FSRP), Perceived Severity (PS), and Perceived Vulnerability (PV). Using a quantitative approach, data was collected from 150 respondents who are users of OFD apps in Jakarta through an online questionnaire. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The results of the study indicate that PU, PSB, and TSB have a significant positive influence on customer intent, while PEOU, TR, FSRP, PS, and PV do not show a significant influence. These findings highlight the importance of practical benefits and efficiency in driving OFD adoption during a health crisis. This study provides insights for OFD business operators to improve marketing strategies and service quality, while contributing to the literature on consumer behavior during the pandemic.

References


Boonsiritomachai, W., & Pitchayadejanant, K. (2019). Determinants affecting mobile banking adoption by generation Y based on the unified theory of acceptance and use of technology model modified by the technology acceptance model concept. Kasetsart Journal of Social Sciences, 40(2), 349–358. https://doi.org/10.1016/j.kjss.2017.10.005

Davis, F. (1986). A Technology Acceptance Model for Empiricaly Testing New End-User Information Systems: Theory and Results. PhDThesis - Massachussetts Institute of Technology.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An

Integrated Model. MIS QUARTERLY, 27(1), 51–90.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson., R. E. (2019). Multivariate Data Analysis : Multivariate Data Analysis : Why multivariate data analysis ? (EIGHT). CENGAGE.

Hair, J. F., Hult, G. T., Ringle, C. M., & Marko Sarstedt. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). https://doi.org/10.1080/1743727x.2015.1005806

Hong, C., Choi, H. (Hailey), Choi, E. K. (Cindy), & Joung, H. W. (David). (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(August), 509–

https://doi.org/10.1016/j.jhtm.2021.08.012

Kuan, H. H., Bock, G. W., & Vathanophas, V. (2008). Comparing the effects of website quality on customer initial purchase and continued purchase at e-commerce websites. Behaviour and Information Technology, 27(1), 3–16. https://doi.org/10.1080/01449290600801959

Lai, P. (2017). The Literature Review of Technology Adoption Models and Theories for the

Novelty Technology. Journal of Information Systems and Technology Management, 14(1),

–38. https://doi.org/10.4301/s1807-17752017000100002

Levine, D. M., Stephan, D. F., Krehbiel, T. C., & Berenson, M. L. (2008). Statistics for Managers: Using Microsoft Excel. In The Statistician (Fifth). Pearson Custom Publishing. https://doi.org/10.2307/2348398

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–

https://doi.org/10.1080/10864415.2003.11044275

Ray, A., & Bala, P. K. (2021). User generated content for exploring factors affecting intention to use travel and food delivery services. International Journal of Hospitality Management,

(October 2020), 102730. https://doi.org/10.1016/j.ijhm.2020.102730

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,

(March), 221–230. https://doi.org/10.1016/j.jretconser.2019.05.025

Sekaran, U., & Bougie, R. (2016). Research Methods for Business (7th ed.). Wiley.

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Journal of Decision Sciences Institute, 39(2), 273–315. https://www.mendeley.com/catalogue/technology-acceptance-model-3-research-agenda- interventions-2/

Venkatesh, V., Davis, F. D., & College, S. M. W. (2000). Theoretical Acceptance Extension

Model:Four Longitudinal Field Studies. Management Science, 46(2), 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information

Technology : Toward A Unifie View. MIS QUARTERLY, 27(3), 425–478. https://doi.org/10.1016/j.inoche.2016.03.015

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theoryof Acceptance and Use of Technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.1109/MWSYM.2015.7167037

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(December 2016), 150–162. https://doi.org/10.1016/j.jretconser.2016.12.013




DOI: http://dx.doi.org/10.35906/equili.v14i2.2556

Article Metrics

Abstract view : 2 times
PDF (Bahasa Indonesia) - 2 times

Copyright © article Equilibrium : Jurnal Ilmiah Ekonomi, Manajemen dan Akuntansi is at the author

Article Equilibrium : Jurnal Ilmiah Ekonomi, Manajemen dan Akuntansi been through a peer review process prior to publication. Journals, editors and editorial boards have the right or obligation to retract articles that contain inaccurate and misleading data if any. Read the Plagiarism Policy and Publication Ethics. Use of this site signifies your agreement to the Terms of Use.

This is an open access article distributed under the terms Creative Commons Attribution-ShareAlike 4.0 International License Creative Commons License.

Website Layout and Editing © 2016 Equilibrium : Jurnal Ilmiah Ekonomi, Manajemen dan Akuntansi on Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Muhammadiyah Palopo.

 

 

Indexing In

 

Tools