BEHAVIORAL INTENTION USING ONLINE FOOD DELIVERY SERVICE: INFORMATION TECHNOLOGY CONTINUANCE APPROACH

Authors

  • Dewi Sri Woelandari Pantjolo Giningroem Universitas Diponegoro (UNDIP), Indonesia; Universitas Bhayangkara Jakarta Raya, Indonesia
  • Naili Farida Universitas Diponegoro (UNDIP), Indonesia
  • Harry Soesanto Universitas Diponegoro (UNDIP), Indonesia

DOI:

https://doi.org/10.33736/ijbs.6416.2023

Keywords:

Hedonic Motivation, Convenience Motivation, Post-UsageUsefullness, Attitude Toward OFD Service, Behavioral Intention Toward OFD Service

Abstract

The surge of online food delivery (OFD) services is emblematic of the restaurant industry's presence in the e-delivery landscape. Further, this research reveals that comprehending consumer decision-making processes necessitates the appraisal of consumer behavior perception. Thus, this study aims to scrutinize the impact of hedonic motivation, convenience motivation, and post-usage usefulness on consumer interest and behavior towards OFD services, moderated by their attitudes towards using such services. To achieve this objective, a purposive sample of 150 students who have used OFD services was gathered and analyzed using a quantitative approach. Empirical data was collected via questionnaires, and a structural equation modeling (SEM-AMOS) was employed to examine the empirical model. The findings of this study suggest that hedonic motivation, convenience motivation, post-usage usefulness, and attitudes towards OFD were key determinants of OFD service behavioral intention. The swift expansion of fast food in Indonesia can be attributed to advancements in telecommunication infrastructure and internet users, lower smartphone prices, as well as the convenience and accessibility of mobile phones and the internet. This phenomenon has been capitalized on by fast food companies, who have leveraged online food delivery (OFD) services to cater to the surging demand.

References

Ajzen, I., Fishbein, M., 1977. Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin. 84 (5), 888-918.

https://doi.org/10.1037/0033-2909.84.5.888

Alavi, S.A., Rezaei, S., Valaei, N., Wan Ismail, W.K., 2016. Examining shopping mall consumer decision-making styles, satisfaction, and purchase intention, The International Review of Retail, Distribution and Consumer Research, 26 (3), 272-303.

https://doi.org/10.1080/09593969.2015.1096808

Anderson, R.E., Srinivasan, S.S., 2003. E-satisfaction and e-loyalty A contigency framework. Psychology and Marketing. 20(3), 123-138.

https://doi.org/10.1002/mar.10063

Babin, B.J., arden, W.R., Griffin, M., 1994. Work ad/or fun: measuring hedonic and utilitarian shopping value, Journal of Consumer Research, 20 (4), 644-656.

https://doi.org/10.1086/209376

Belanche, D., Casalo, L.V., Guinaliu, M., 2012. Website usability, consumer satisfaction and the intention to use a website: the moderating effect of perceived risk. Journal of Retailing and Consumer Service, 19 (1), 124-132.

https://doi.org/10.1016/j.jretconser.2011.11.001

Bilgihan, A., 2016. Gen Y customer loyalty in online shopping: an integrated model of trust, user experience and branding. Comput. Hum. Behav. 61. 103-113.

https://doi.org/10.1016/j.chb.2016.03.014

Chai, L. T., Yat, D. N. C., 2019. Online Food Delivery Service: Making Food Delivery the New Normal. Journal of Marketing Advances and Practices. 1 (1). 63-77.

Chang, C.-C., Yan, D.-F., Tseng, J.-S., 2012. Perceived convenience in an extended technology acceptance model: mobile technology and Engllish learning for college students. Australas. J. Educ. Technol. 28(5). 809-826.

https://doi.org/10.14742/ajet.818

Chang, S.-C., Chen, H.-H., Chen, M.-F., 2008. Determinants of satisfaction and continuance intention towards self-service technologies, Ind. Manag. Data Syst. 109 (9), 1248-1263.

https://doi.org/10.1108/02635570911002306

Childers, T.I., Carr, C.L., Peck, J., Carson, S., 2002. Hedonic and utilitarian motivations for online retail shopping behavior. J. Retail. 77 (4), 511-535.

https://doi.org/10.1016/S0022-4359(01)00056-2

Cho, M., Bonn, M. A., Li, J. J., (2018). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management. 0278-4319. 1-9

Holbrook, M.B., Hirschman, E.C., 1982. The experiental aspets of consumption: consumer fantasies, feelings and fun. Journal of Consumer Research, 9 (2), 132-140.

https://doi.org/10.1086/208906

Jeng, S.-P., 2016. The influences of airline brand credibility on consumer purchase intentions. Journal of Air Transport Management, 55, 1-8.

https://doi.org/10.1016/j.jairtraman.2016.04.005

Kang, J., Park-Poaps, H., 2010. Hedonic and utilitarian shopping motivations of fashion leadership. Journal Fashion Marketing and Management, 14(2), 312-328.

https://doi.org/10.1108/13612021011046138

Kimes, S.E., 2011. The current state of online food ordering in the U.S restaurant industry. Cornell Hospitality Report. 11(17), 6-18. Lee, S. W., 2019. Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality. Sustainability, 11, 3141, 1-15.

https://doi.org/10.3390/su11113141

Monroe, K.B., Lee, A.Y., 1999. Remembering versus knowing: issues in buyers processing of price information. Journal of Academy of Marketing Science, 27(2), 207-225.

https://doi.org/10.1177/0092070399272006

Muller, C. (2018), "Restaurant delivery: Are 'ODP' the industry's 'OTA'? Part I. October 31", available at: www.bu.edu/bhr/2018/10/31/restaurant-delivery-arethe-odp-the-industrys-ota-part-i/Park, E., Kim, K.J., 2013. User acceptance of long-term evolution (LTE) services: an application of extended technology acceptance model. Program 47 (2), 188-205.

https://doi.org/10.1108/00330331311313762

Pinho, J.C.M.R., Soares, A.M., 2011. Examining the technology acceptance model in the adoption of social networks. Journal of Research in Interactive Marketing, 5(2/3), 116-129.

https://doi.org/10.1108/17505931111187767

Rezaei. S., Ghodsi, S.S. 2014. Does value matters in playing online game? An empirical study among massively multiplayer online role-playing games (MMOPGs). Computer in Human Behavior, 35, 252-266.

https://doi.org/10.1016/j.chb.2014.03.002

Rezaei, S., Ali, F., Amin, M., Jayashree, S. (2016) 3G post adoption users experience with telecommunications services: a partial least squares (PLS) path modelling approach, Nankai Business Review International, 7(3), 361-394.

https://doi.org/10.1108/NBRI-01-2016-0007

Rezaei, S., Shahijan, M.K., Amin, M., Ismail, W.K.W., 2016c. Determinants of app stores continuance behavior: a PLS path modelling approach. Journal of Internet Commerce, 15 (4), 408-440.

https://doi.org/10.1080/15332861.2016.1256749

Ullal, M. S., Hawaldar, I. T., Mendon, S., & Joseph, N. (2020). The effect of artificial intelligence on the sales graph in the Indian market. Entrepreneurship and Sustainability Issues, 7(4), 2940-2954.

https://doi.org/10.9770/jesi.2020.7.4(24)

Wu, S.-I., 2003. The relationship between consumer characteristics and attitude toward online shopping. Marketing Intelligence and Planning, 21(1), 37-44

https://doi.org/10.1108/02634500310458135

Downloads

Published

2023-12-26

How to Cite

Dewi Sri Woelandari Pantjolo Giningroem, Naili Farida, & Harry Soesanto. (2023). BEHAVIORAL INTENTION USING ONLINE FOOD DELIVERY SERVICE: INFORMATION TECHNOLOGY CONTINUANCE APPROACH. International Journal of Business and Society, 24(3), 1186–1196. https://doi.org/10.33736/ijbs.6416.2023