Factors Affecting Intention to Use Food Order-Delivery Feature of Ride-Hailing Applications: The UTAUT Approach

  • Ade Permata Surya Faculty of Economics and Business, Universitas Diponegoro and Faculty of Economics and Business, Universitas Mercu Buana
  • I Made Sukresna Faculty of Economics and Business, Universitas Diponegoro
  • Aris Mardiyono Faculty of Economics and Business, Universitas Diponegoro
Keywords: Behavioral intention, UTAUT, Ride-hailing applications, Food order-delivery feature

Abstract

Since ride-hailing platforms in Indonesia provide food order-delivery feature, there has been a change in public spending behavior from conventional to electronic, even on the most basic element, i.e., food. The purpose of this study is to identify critical factors affecting consumers’ adoption of ride-hailing applications to buy food using the Unified Theory of Acceptance and Use of Technology (UTAUT) approach. This study uses a cross-sectional design with a non-probability sampling method. Data was collected from self-administered questionnaire, resulting a total sample of 315 respondents across Indonesia. The respondents are GrabFood and GoFood consumers and the food order-delivery feature of Grab and Gojek (the ride-hailing applications). The study employs PLS-SEM technique to analyze the relationships among variables. The findings show performance expectancy, social influence, and facilitating conditions positively influence behavioral intention to use food order-delivery features in ride-hailing applications. On the other hand, effort expectancy does not influence behavioral intention. The results corroborate the role of food-order feature of ride-hailing applications in the change of Indonesian consumer behavior. Referring to the study results, theoretical contributions and practical implications are provided.

References

Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665-694. https://doi.org/10.2307/3250951

Acheampong, R. A., Siiba, A., Okyere, D. K., & Tuffour, J. P. (2020). Mobility-on-demand: An empirical study of internet-based ride-hailing adoption factors, travel characteristics and mode substitution effects. Transportation Research Part C: Emerging Technologies, 115, 102638. https://doi.org/10.1016/j.trc.2020.102638

Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44. https://doi.org/10.1016/j.ijinfomgt.2019.04.008

Alharbi, N., Papadaki, M. & Dowland, P. (2017). The impact of security and its antecedents in behaviour intention of using e-government services. Behaviour & Information Technology, 36(6), 620-636. https://doi.org/10.1080/0144929X.2016.1269198

Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020). Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility. Sustainability, 12(10), 4275. https://doi.org/10.3390/su12104275

Biehl, A., Ermagun, A., & Stathopoulos, A. (2019). Utilizing multi-stage behavior change theory to model the process of bike share adoption. Transport Policy, 77, 30-45. https://doi.org/10.1016/j.tranpol.2019.02.001

Cheng, X., Fu, S., & deVreede, G.-J. (2018). A mixed method investigation of sharing economy driven car-hailing services: Online and offline perspectives. International Journal of Information Management, 41, 57-64. https://doi.org/10.1016/j.ijinfomgt.2018.03.005

Chua, P. Y., Rezaei, S., & Gu, M.-L. (2018). Elucidating social networking apps decisions. Nankai Business Review International, 9(2), 118-142. https://doi.org/10.1108/NBRI-01-2017-0003

Contreras, S. D., & Paz, A. (2018). The effects of ride-hailing companies on the taxicab industry in Las Vegas, Nevada. Transportation Research Part A: Policy and Practice, 115, 63-70. https://doi.org/10.1016/j.tra.2017.11.008

Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies. Interactive Technology and Smart Education, 14(4), 329-348. https://doi.org/10.1108/ITSE-06-2016-0015

Fleury, S., Tom, A., Jamet, E., & Colas-Maheux, E. (2017). What drives corporate carsharing acceptance? A French case study. Transportation Research Part F: Traffic Psychology and Behaviour, 45, 218-227. https://doi.org/10.1016/j.trf.2016.12.004

Friman, M., Huck, J., & Olsson, L. E. (2017). Transtheoretical Model of Change during Travel Behavior Interventions: An Integrative Review. International Journal of Environmental Researchand Public Health, 14(6), 581. https://doi.org/10.3390/ijerph14060581

Hair, F. J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed). Los Angeles: SAGE.

He, F. & Shen, Z.-J. M. (2015). Modeling taxi services with smartphone-based e-hailing applications. Transportation Research Part C: Emerging Technologies, 58, 93-106. https://doi.org/10.1016/j.trc.2015.06.023

He, F., Wang, X., Lin, X., & Tang, X. (2018). Pricing and penalty/compensation strategies of a taxi-hailing platform. Transportation Research Part C: Emerging Technologies, 86, 263-279. https://doi.org/10.1016/j.trc.2017.11.003

Indonesian Digital Report. (2020). Digital 2020: Indonesia. https://datareportal.com/reports/digital-2020-indonesia

Howard, R., Restrepo, L., & Chang, C.-Y. (2017). Addressing individual perceptions: An application of the unified theory of acceptance and use of technology to building information modelling. International Journal of Project Management, 35(2), 107-120. https://doi.org/10.1016/j.ijproman.2016.10.012

Isaac, O., Abdullah, Z., Aldholay, A. H., & Abdulbaqi Ameen, A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pacific Management Review, 24(4), 335-354. https://doi.org/10.1016/j.apmrv.2018.12.003

Jewer, J. (2018). Patients' intention to use online postings of ED wait times: A modified UTAUT model. International Journal Medicine Informatics, 112, 34-39. https://doi.org/10.1016/j.ijmedinf.2018.01.008

Joia, L. A., & Altieri, D. (2018). Antecedents of continued use intention of e-hailing apps from the 'passengers' perspective. The Journal of High Technology Management Research, 29(2), 204-215. https://doi.org/10.1016/j.hitech.2018.09.006

Karulkar, Y., Pahuja, J., Uppal, B. S., & Sayed, S. (2019). Examining UTAUT model to explore consumer adoption in Online Food Delivery (OFD) services. Pramana Research Journal, 9(8), 146-162.

Kurfalı, M., Arifoğlu, A., Tokdemir, G., & Paçin, Y. (2017). Adoption of e-government services in Turkey. Computers in Human Behavior, 66, 168-178. https://doi.org/10.1016/j.chb.2016.09.041

Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality. Sustainability, 11(11), 3141. https://doi.org/10.3390/su11113141

Lu, K., & Wang, X. (2020). Analysis of Perceived Value and Travelers' Behavioral Intention to Adopt Ride-Hailing Services: Case of Nanjing, China. Journal of Advanced Transportation, 2020, 1-13. https://doi.org/10.1155/2020/4380610

Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7), 738-756. https://doi.org/10.1108/ET-02-2014-0014

Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935-948. https://doi.org/10.1016/j.chb.2017.06.013

Mannan, B., & Haleem, A. (2017). Understanding major dimensions and determinants that help in diffusion & adoption of product innovation: using AHP approach. Journal of Global Entrepreneurship Research, 7(12),1-24. https://doi.org/10.1186/s40497-017-0072-4

Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The Influence of Values on E-learning Adoption. Computers & Education, 141, 103617. https://doi.org/10.1016/j.compedu.2019.103617

Morosan, C., & DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers' intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29. https://doi.org/10.1016/j.ijhm.2015.11.003

Naranjo-Zolotov, M., Oliveira, T., & Casteleyn, S. (2019). Citizens' intention to use and recommend e-participation. Drawing upon UTAUT and citizen empowerment. Information Technology & People, 32(2), 364-386. https://doi.org/10.1108/ITP-08-2017-0257

Nielsen Admosphere. (2019). ABCDE Socio-economic Classification Specification for year 2020. https://www.nielsen-admosphere.eu/wp-content/uploads/2019/12/Nielsen-Admosphere-ABCDE-classification-specification 2020.pdf

Okumus, B., Ali, F., Bilgihan, A., & Ozturk, A. B. (2018). Psychological factors influencing customers' acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, 72, 67-77. https://doi.org/10.1016/j.ijhm.2018.01.001

Pigatto, G., Machado, J. G. C. F., Negreti, A. S., & Machado, L. M. (2017). Have you chosen your request? Analysis of online food delivery companies in Brazil. British Food Journal 119(3), 639-657. https://doi.org/10.1108/BFJ-05-2016-0207

Rahi, S., & Ghani, M., A. (2019). Investigating the role of UTAUT and e-service quality in internet banking adoption setting. The TQM Journal, 31(3), 491-506. https://doi.org/10.1108/TQM-02-2018-0018

Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2016). Adoption of online public grievance redressal system in India: Toward developing a unified view. Computers in Human Behavior, 59, 265-282. https://doi.org/10.1016/j.chb.2016.02.019

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

Reyes-Mercado, P. (2018). Adoption of fitness wearables: Insights from Partial Least Squares and Qualitative Comparative Analysis. Journal of Systems and Information Technology, 20(1), 103-127. https://doi.org/10.1108/JSIT-04-2017-0025

Rondan-Cataluña, F. J., Arenas-Gaitán, J., & Ramírez-Correa, P. E. (2015). A comparison of the different versions of popular technology acceptance models A non-linear perspective. Kybernetes, 44(5), 788-805. https://doi.org/10.1108/K-09-2014-0184

Shiferaw, K. B., & Mehari, E. A. (2019). Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics in Medicine Unlocked, 17, 100182. https://doi.org/10.1016/j.imu.2019.100182

Singh, A. K., & Thirumoorthi, P. (2019). The Impact of Digital Disruption Technologies on Customer Preferences: The Case of Retail Commerce. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 1255-1261. https://doi.org/10.35940/ijrte.C4404.098319

Soh, P. Y., Heng, H. B., Selvachandran, G., Anh, L.Q., Chau, H.T.M., Son, L.H., Abdel-Baset, M., Manogaran, G., & Varatharajan, R. (2020). Perception, acceptance and willingness of older adults in Malaysia towards online shopping: a study using the UTAUT and IRT models. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01718-4

Suki, N. M., & Suki, N. M. (2017). Determining students' behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528-538. https://doi.org/10.1016/j.ijme.2017.10.002

Sumak, B., & Sorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602-620. https://doi.org/10.1016/j.chb.2016.07.037

Talwar, S., Talwar, M., Kaur, P., & Dhir, A. (2020). Consumers' Resistance to Digital Innovations: A Systematic Review and Framework Development. Australasian Marketing Journal, 28(4), 286-299. https://doi.org/10.1016/j.ausmj.2020.06.014

Taylor. S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Model. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal Computing Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 125-143. https://doi.org/10.2307/249443

Valenta, E. (2019). Food order applications change the behavior of Indonesian consumers. https://beritagar.id/artikel/berita/aplikasi-order-makanan-ubah-perilaku-konsumen-indonesia.

Venkatesh, V., Morris, M.G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Vivoda, J. M., Harmon, A. C., Babulal, G. M., Zikmund-Fisher, B. J. (2018). E-hail (Rideshare) Knowledge, Use, Reliance, and Future Expectations among Older Adults. Transportation Research Part F: Traffic Psychology Behaviour, 55, 426-434. https://doi.org/10.1016/j.trf.2018.03.020

Wang, Y., Wang, S., Wang, J., Wei, J., & Wang, C. (2020). An empirical study of consumers' intention to use ride-sharing services: using an extended technology acceptance model. Transportation, 47(1), 397-415. https://doi.org/10.1007/s11116-018-9893-4

Wong, K. T., Russo, S., & McDowall, J. (2013). Understanding early childhood student teachers' acceptance and use of interactive whiteboard. Campus-Wide Information Systems, 30(1), 4-16. https://doi.org/10.1108/10650741311288788

Yaseen, S. G., & Qirem, I. A. E. (2018). Intention to use e-banking services in the Jordanian commercial banks International Journal of Bank Marketing, 36(3), 557-557. https://doi.org/10.1108/IJBM-05-2017-0082

Yu, X., Gao, S., Hu, X., & Hyoshin, P. (2019). A Markov decision process approach to vacant taxi routing with e-hailing. Transportation Research Part B: Methodological, 121, 114-134. https://doi.org/10.1016/j.trb.2018.12.013

Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal Hospital Management, 91, 102683. https://doi.org/10.1016/j.ijhm.2020.102683

Zhou, M., Zhao, L., Kong, N., Campy, K. S., Xu, G., Zhu, G., Cao, X., & Wang, S. (2020). Understanding consumers' behavior to adopt self-service parcel services for last-mile delivery. Journal of Retailing and Consumer Services, 52, 101911. https://doi.org/10.1016/j.jretconser.2019.101911

Published
2021-12-17
How to Cite
Ade Permata Surya, I Made Sukresna, & Aris Mardiyono. (2021). Factors Affecting Intention to Use Food Order-Delivery Feature of Ride-Hailing Applications: The UTAUT Approach. International Journal of Business and Society, 22(3), 1363-1383. https://doi.org/10.33736/ijbs.4306.2021