Studying Technology Adoption Inhibition in the Context of Food Ordering Apps
DOI:
https://doi.org/10.12725/ujbm.42.1Keywords:
Technology adoption model, Inhibitors, Insecurity, Discomfort, Infrastructure, Inertia, Food ordering appsAbstract
Food ordering apps are dramatically changing the out-of-home food consumption. With mobile phones emerging as the ubiquitous self-help device, a new wave of food ordering is evolving. This study aims at identifying the technology adoption inhibitors that consumers face when they migrate from dine-out experience to online take-home experience. We propose insecurity, discomfort, infrastructure and inertia as four inhibitors for a full-scale migration to online food ordering and establish that all four variables are significant in explaining the current consumer inhibition in the area of the study mentioned. While adoption, acceptance and readiness for technology usage are given attention and focus, this study stands out as an analysis of consumer inhibition patterns.
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2. Lin, C., Jiun-Sheng, & Hsieh, P. (2006). The role of technology readiness in customers' perception and adoption of self-service technologies. International Journal of Service Industry Management, 17(5), 497-517.
3. Culnan, M.J., & Armstrong, P.K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: an empirical investigation. Organization Science, 10(1), 104–115.
4. Dabholkar, P. A. (1995). Consumer evaluations of new technology-based self-service options: an investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), 29-51.
5. Dabholkar, P. A., & Spaid, B. I. (2012). Service failure and recovery in using technology-based self-service: effects on user attributions and satisfaction. Service Industries Journal, 32(9), 1415-1432.
6. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
7. Durkin, M. (2004). In search of the internet-banking customer: exploring the use of decision styles. International Journal of Bank Marketing, 22(7), 484-503.
8. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley Publishing Company.
9. Hoffman, D.L., Novak, T.P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80–85.
10. Hyunsuk, I., Jung, J., Kim, Y., & Shin, D-H. (2014). Factors affecting resistance and intention to use the smart TV. Journal of Media Business Studies, 11(3), 23-42.
11. Iyer, E.K., Unnikrishnan, S., Philip, P., & Sundararajan, M. (2015). Extending technology adoption model by addition of cognitive inhibitors. Proceedings of International Conference on Business Management & Information Systems, 89-95.
12. Joseph, R. C. (2010). Individual resistance to IT innovations. Communications of the ACM, 53(4), 144-146.
13. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing, 24(7), 419-427.
14. Lee, H.G. (1998). Do electronic marketplaces lower the price of goods?. Communications of the ACM, 41(1), 73–80.
15. Meuter, M.L., Ostrom, A.L., Bitner, M.J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906.
16. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M.J. (2000). Self-service technologies: understanding customer satisfaction with technology based service encounters. Journal of Marketing, 64(3), 50-64.
17. Osgood, C. E., & Tannenbaum, P. H. (1955). The principle of congruity in the prediction of attitude change. Psychological Review, 62(1), 42-55.
18. Parasuraman, A. (2000). Technology readiness index (TRI): a multiple item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.
19. Ram, S. (1987). A model of innovation resistance. Advances in Consumer Research, 14(1), 208-212.
20. Schnellbächer, C., Behr, J., Leonhäuser, I-U., & Gießen. (2015). Potential of online food shopping: An opportunity to relieve mothers’ everyday life food routines?. Ernahrungs Umschau, 62(11) 178–187.
21. Wakefield, R. L., Whitten, D. (2006). Mobile computing: a user study on hedonic/utilitarian mobile device usage. European Journal of Information Systems, 15(3), 292-300.
22. Shih, Y., & Fang, K. (2006). Effects of network quality attributes on customer adoption intentions of Internet Banking. Total Quality Management & Business Excellence, 17(1), 61-77.
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Published
2021-08-30
How to Cite
Iyer, E. K., Gujral, A., Raundal, A., & Saxena, H. (2021). Studying Technology Adoption Inhibition in the Context of Food Ordering Apps. Ushus Journal of Business Management, 17(1), 1-14. https://doi.org/10.12725/ujbm.42.1
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Copyright (c) 2019 Easwar Krishna Iyer, Anchit Gujral, Anuja Raundal, Hardik Saxena
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
