Integrating Planned Behavior and Technology Acceptance Models: Study of GoFood after COVID-19

Authors

  • Felicia Margaret Universitas Ciputra
  • Helena Sidharta Universitas Ciputra

DOI:

https://doi.org/10.21512/tw.v24i2.10847

Keywords:

intention to use GoFood, online food delivery services, technology acceptance model, theory of planned behavior

Abstract

As the Covid-19 pandemic has accelerated the process of digitalization, the usage of technology level remains high even when the pandemic has already over. This was proved by the high level of internet penetration rate and the total number of internet users, especially in Indonesia. One of the most obvious changes are the increase of online food delivery services usage which have been a part of people’s lifestyle. This study will explore the key factors that influence customers’ intention to use GoFood by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to build the research model. From this model, underlying factors behind the customers’ intention to use GoFood will be defined by price saving orientation and time saving orientation with a mediating variable called convenience motivation. Afterwards, 252 valid questionnaires from respondents who live in Surabaya and Sidoarjo were collected and analyzed using the SEM-PLS method. The result from the analysis revealed that price saving orientation is the most significant factors behind customers’ intention to use. Meanwhile, time saving orientation only have indirect effect towards customers’ intention to use which means it will only be impactful when it is mediated by the convenience motivation. Moreover, convenience motivation itself turns out to have significant impact toward customers’ intention to use.

Dimensions

Plum Analytics

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.

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324. https://doi.org/10.1002/hbe2.195.

Asosiasi Penyelenggara Jasa Internet Indonesia. (2022). Profil Internet Indonesia 2022. https://apjii.or.id/download_survei/2feb5ef7-3f51-487d-86dc-6b7abec2b171.

Bosnjak, M., Ajzen, I., & Schmidt, P. (2020). The theory of planned behavior: Selected recent advances and applications. In Europe’s Journal of Psychology, 16(3), 352-356. https://doi.org/10.5964/ejop.v16i3.3107.

Chakraborty, S., Azam, M. K., & Sana. (2022). Factors Affecting the Behavioural Intentions of Indian Millennials. International Journal of Online Marketing, 12(1), 1-16. https://doi.org/10.4018/ijom.306975.

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 and Tourism Marketing, 38(1), 16-30. https://doi.org/10.1080/10548408.2020.1862023.

Chuttur, M. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Working Papers on Information Systems, 1-21.

Erfinanto, E. (2021, October 7). Lebih dekat dengan Surabaya, kota metropolitan terbesar kedua di Indonesia. Liputan6. https://www.liputan6.com/surabaya/read/4677538/lebih-dekat-dengan-surabaya-kota-metropolitan-terbesar-kedua-di-indonesia

Giningroem, D. S. W. P., Setyawati, N. W., & Wijayanti, M. (2022). Consumer experiences, time saving orientation, and price saving orientation on actual behavior to use application online food delivery through convenience motivation. East Asian Journal of Multidisciplinary Research, 1(11), 2549-2560. https://doi.org/10.55927/eajmr.v1i11.1989.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd Ed.). SAGE Publications.

Hakim, M. R., & Sobari, N. (2022). Factors influencing consumers’ attitude and repurchase intention towards online food delivery (OFD) services in Indonesia. Contemporary Research on Business and Management, 161-165.

Hastings, J., & Shapiro, J. M. (2012). Mental accounting and consumer choice: Evidence from commodity price shocks. (Working Paper 18248), 1-49. https://doi.org/10.3386/w18248.

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, 509-518. https://doi.org/10.1016/j.jhtm.2021.08.012.

Hooi, R., Kin Leong, T., Hui Yee, L., & Rahman, A. (2021). Intention to use online food delivery service in Malaysia among university students. Conference on Management, Business, Innovation, Education and Social Science, 1(1). https://journal.uib.ac.id/index.php/combines

Inthong, C., Champahom, T., Jomnonkwao, S., Chatpattananan, V., & Ratanavaraha, V. (2022). Exploring factors affecting consumer behavioral intentions toward online food ordering in Thailand. Sustainability, 14(14). https://doi.org/10.3390/su14148493.

Irawan, M. Z., Bastarianto, F. F., & Priyanto, S. (2022). Using an integrated model of TPB and TAM to analyze the pandemic impacts on the intention to use bicycles in the post-COVID-19 period. IATSS Research, 46(3), 380-387. https://doi.org/10.1016/j.iatssr.2022.05.001.

Javier, F. (2021, December 24). GoFood platform pesan antar makanan paling banyak digunakan. Tempo. https://data.tempo.co/data/1295/gofood-platform-pesan-antar-makanan-paling-banyak-digunakan

Karim, A., Asrianto, Ruslan, M., & Said, M. (2023). Gojek accelerate economic recovery through the digitalization of MSMEs in Makassar. The Winners, 24(1), 23. https://doi.org/10.21512/tw.v24i1.9388.

Kartono, R., & Tjahjadi, J. K. (2021). Investigating factors affecting consumers’ intentions to use online food delivery services during Coronavirus (COVID-19) outbreak in Jabodetabek Area. The Winners, 22(1). https://doi.org/10.21512/tw.v22i1.6822.

Kenang, I. H., & Gosal, G. (2021). Factors affecting online donation intention in donation-based crowdfunding. The Winners, 22(2). https://doi.org/10.21512/tw.v22i2.7101.

Kusumawardhani, S. (2022). Survei Persepsi & Perilaku Konsumsi Online Food Delivery (OFD) di Indonesia. Tenggara Strategics. https://asset.tenggara.id/assets/source/file-research/OFD/Tenggara_Strategics_-_2022_OFD_Research_-_Presentation.pdf

Larasati, R. A., & Jatmiko, B. P. (2019, October 4). GoFood catat peningkatan pengguna. Kompas.com. https://money.kompas.com/read/2019/10/04/170949226/gofood-catat-peningkatan-pengguna

Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12. https://doi.org/10.17705/1cais.01250.

Leong, M. K., & Koay, K. Y. (2023). Towards a unified model of consumers’ intentions to use drone food delivery services. International Journal of Hospitality Management, 113. https://doi.org/10.1016/j.ijhm.2023.103539

Measurable AI. (2022). Asia Online Delivery Report: Food + Grocery. Kowloon: Measurable AI. https://bit.ly/3ReMLdW

Nguyen, T. T. H., Nguyen, N., Nguyen, T. B. L., Phan, T. T. H., Bui, L. P., & Moon, H. C. (2019). Investigating consumer attitude and intention towards online food purchasing in an emerging economy: An extended TAM approach. Foods, 8(11). https://doi.org/10.3390/foods8110576.

Novita, D., & Husna, N. (2020). The influence factors of consumer behavioral intention towards online food delivery services. Technobiz, 3(2), 40-42. https://doi.org/10.33365/tb.v3i2.840.

Perdana, D. (2018, November 12). Surabaya peringkat kedua jumlah transaksi Go-Food terbanyak. Suara Surabaya. https://www.suarasurabaya.net/ekonomibisnis/2018/Surabaya-Peringkat-Kedua-Jumlah-Transaksi-Go-Food-Terbanyak/

Perwitasari, A. W. (2022). The effect of perceived usefulness and perceived easiness towards behavioral intention to use fintech by Indonesian MSMEs. The Winners, 23(1), 1-9. https://doi.org/10.21512/tw.v23i1.7078.

Pitchay, A. A., Ganesan, Y., Zulkifli, N. S., & Khaliq, A. (2022). Determinants of customers’ intention to use online food delivery application through smartphone in Malaysia. British Food Journal, 124(3), 732-753. https://doi.org/10.1108/BFJ-01-2021-0075.

Pranoto, A. H., & Lumbantobing, P. (2021). The acceptance technology model for adoption of social media marketing in Jabodetabek. The Winners, 22(1). https://doi.org/10.21512/tw.v22i1.7073.

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), 1-17. https://doi.org/10.3390/joitmc7010076.

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.

Statista. (2023). Online Food Delivery - Market Data Analysis & Forecast. Hamburg: Statista. https://www.statista.com/outlook/dmo/online-food-delivery/worldwide

Surendran, P. (2012). Technology acceptance model: A survey of literature. International Journal of Business and Social Research, 2(4), 175-178. https://doi.org/10.18533/ijbsr.v2i4.161.

Tran, V. D. (2021). Using mobile food delivery applications during the covid‐19 pandemic: Applying the theory of planned behavior to examine continuance behavior. Sustainability, 13(21). https://doi.org/10.3390/su132112066.

Troise, C., O’Driscoll, A., Tani, M., & Prisco, A. (2021). Online food delivery services and behavioural 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.

Vu, T. D., Nguyen, H. V., Vu, P. T., Tran, T. H. H., & Vu, V. H. (2023). Gen Z Customers’ Continuance Intention in Using Food Delivery Application in an Emerging Market: Empirical Evidence from Vietnam. Sustainability, 15(20), 14776. https://doi.org/10.3390/su152014776.

Wong, K. K.-K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24. http://marketing-bulletin.massey.ac.nz.

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, 150-162. https://doi.org/10.1016/j.jretconser.2016.12.013.

Zanetta, L. D. A., Hakim, M. P., Gastaldi, G. B., Seabra, L. M. A. J., Rolim, P. M., Nascimento, L. G. P., …, & da Cunha, D. T. (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. https://doi.org/10.1016/j.foodres.2021.110671.

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Published

2023-12-18

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Section

Digital Transformation from Marketing Perspective in Developing Countries
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