The Journal of Economics, Marketing and Management (융합경영연구)

International Convergence Management Association (국제융합경영학회)
권호 표지
DOI QR코드

DOI QR Code

The Effect of Perceive Ease of Use, Perceive Usefulness and Perceive Risk towards Behavioral Intention of GO-FOOD Customer in Indonesia

  • Received : 2022.05.05
  • Accepted : 2022.08.05
  • Published : 2022.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: Technology and innovation drive new mobile application for ojek online. Using the theory of technology acceptance model and perceived risk theory, the researcher wants to find how these factors affect user's intention to use GO-FOOD that leads to technology adoption. Research design, data and methodology: The researcher uses GO-FOOD users that located in East Java, Indonesia for the object of study. Results: The findings of the research discovered that perceive usefulness and perceive ease of use do not significantly affect user's behavioral intention while perceive risk is significantly affecting the user's behavioral intention. Conclusions: The findings suggested that GO-FOOD or similar application should focus more on reducing or eliminating user's perception of risk towards the mobile application

Keywords

References

  1. Abdurrahman, M. S. (2017, August 14). 2018, Transaksi e-Commerce Indonesia Akan Capai Rp 144 Triliun. Liputan 6. Retrieved August 23, 2018, from https://www.liputan6.com/tekno/read/3057134/2018-transaksi-e-commerce-indonesia-akan-capai-rp-144-triliun
  2. Alalwan, A. A., Dwivedi, Y. K., Rana, N. P. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan. Journal of Enterprise Information Management, 29(1), 118-139. doi:10.1108/jeim-04-2015-0035
  3. Ali, M., Raza, S. A., & Puah, C.-H. (2017). Factors affecting to select Islamic credit cards in Pakistan: the TRA model. Journal of Islamic Marketing, 8(3), 330-344. doi:10.1108/jima-06-2015-0043
  4. Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management, 45(6), 626-640. doi:10.1108/ijrdm-08-2016-0144
  5. Cabanillas, L. F., Marinkovic, V., & Kalinic, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14-24. https://doi.org/10.1016/j.ijinfomgt.2016.10.008
  6. Cahya, P. (2018, February 17). Makin Ketat, Begini Persaingan Tiga Transportasi Online Terbesar di Indonesia. IDN Times. Retrieved August 23, 2018, from https://www.idntimes.com/business/economy/putriana-cahya/persaingan-tiga-transportasi-online-terbesar/full
  7. Chong, A. Y.-L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240-1247. doi:10.1016/j.eswa.2012.08.067
  8. Childs, R. D., Gingrich, G., & Piller, M. (2009). The future workforce: Gen Y has arrived. Public manager, 38(4), 21.
  9. Dauda, S. Y., & Lee, J. (2015). Technology adoption: A conjoint analysis of consumers preference on future online banking services. Information Systems, 53, 1-15. https://doi.org/10.1016/j.is.2015.04.006
  10. Dailysocialid. (2017, September 12). Gabung GO-FOOD Pengusaha UKM Kuliner Tingkatkan Penjualan Hingga 345% | Dailysocial. Retrieved from https://dailysocial.id/wire/gabung-go-food-pengusaha-ukm-kuliner-tingkatkan-penjualan-hingga-345/
  11. Eastman, J. K., Iyer, R., Liao-Troth, S., Williams, D. F., & Griffin, M. (2014). The Role of Involvement on Millennials' Mobile Technology Behaviors: The Moderating Impact of Status Consumption, Innovation, and Opinion Leadership. Journal of Marketing Theory and Practice, 22(4), 455-470. doi:10.2753/mtp1069-6679220407
  12. Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of Convenience Sampling and Purposive Sampling. 5(1), 1-4. doi:10.11648/j.ajtas.20160501.1
  13. Field, A. (2015). Discovering Statistics Using IBM SPSS Statistics. London: Sage Publications.
  14. Gibson, L., & Sodeman, W. A. (2014). Millennials and Technology: Addressing the Communication Gap in Education and Practice. Retrieved November 10, 2018, from https://www.researchgate.net/publication/268224500.
  15. Gu, J.C., Lee, S.C and Suh, Y.H (2009), Determinants of behavioral intention to mobile banking, Expert Systems with Applications, 36(9), 11605-11616. https://doi.org/10.1016/j.eswa.2009.03.024
  16. Hamid, A. A., Razak, F. Z. A., Bakar, A. A., & Abdullah, W. S. W. (2016). The Effects of Perceived Usefulness and Perceived Ease of Use on Continuance Intention to Use E-Government. Procedia Economics and Finance, 35, 644-649. doi:10.1016/s2212-5671(16)00079-4
  17. Hongdiyanto, C., Teofilus, Sutrisno, T. F. C. W. & Dewanti, P. S. P. (2020). The Effect of Entrepreneurial Learning towards Entrepreneurial Intention of Indonesian Women, Journal of Asian Finance, Economics and Business, 7(9), 573-582 https://doi.org/10.13106/JAFEB.2020.VOL7.NO9.573
  18. Hussain, A., Mkpojiogu, E. O. C., & Yusof, M. M. (2016). Perceived usefulness, perceived ease of use, and perceived enjoyment as drivers for the user acceptance of interactive mobile maps. doi:10.1063/1.4960891
  19. Kock, J. (2015). The Technology Acceptance Model (TAM). An Overview. German: German National Library.
  20. Muk, A., & Chung, C. (2015). Applying the technology acceptance model in a two-country study of SMS advertising. Journal of Business Research, 68(1), 1-6. doi:10.1016/j.jbusres.2014.06.001
  21. Munoz-Leiva, F., Climent-Climent, S., & Liebana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25-38. doi:10.1016/j.sjme.2016.12.001
  22. Otieno, O. C., Liyala, S., Ondogo, B. C., & Abeka, S. (2016). Theory of Reasoned Action as an Underpinning to Technological Innovation Adoption Studies. World Journal of Computer Application and Technology 4(1): 1-7. DOI: 10.13189/wjcat.2016.040101
  23. Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat. International Journal of Bank Marketing, 36(1), 147-169. doi:10.1108/ijbm-08-2016-0104
  24. Priyatno, D. (2014). SPSS 22 Pengolahan Data Terpraktis.Yogyakarta: CV Andi Offset.
  25. Septiani, R., Handayani, P. W., & Azzahro, F. (2017). Factors that Affecting Behavioral Intention in Online Transportation Service: Case study of GO-JEK. Procedia Computer Science, 124, 504-512. doi:10.1016/j.procs.2017.12.183
  26. Sugiyono. (2015). Metode Penelitian Manajemen. Bandung: Alfabeta.
  27. Supriyatna, I. (2016, November 7). BPS Sebut "Ojek Online" Mampu Tekan Angka Pengangguran - Kompas.com. Retrieved from https://ekonomi.kompas.com/read/2016/11/07/175237426/bps.sebut.ojek.online.mampu.tekan.angka.pengangguran
  28. Upadhyay, P., & Jahanyan, S. (2016). Analyzing user perspective on the factors affecting use intention of mobile based transfer payment. Internet Research, 26(1), 38-56. doi:10.1108/intr-05-2014-0143
  29. Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253-269. doi.org/10.1108/IMDS-08-2014-0243.
  30. Lee, J. W., Kwag, M., & Potluri, R. M. (2015). Antecedents of Acceptance of Social Networking Sites in Retail Franchise and Restaurant Businesses. Journal of Asian Finance, Economics and Business, 2(1), 29-36. https://doi.org/10.13106/KODISA Journals.2015.vol2.no1.29.