ONLINE FOOD DELIVERY APPLICATIONS: QUALITATIVE INQUIRY OF DIMENSIONS IMPACTING CONSUMER INTENTIONS AND USAGE
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The study explores different dimensions impacting Food Delivery apps' usage in Tier 1, 2, and 3 cities in India. The study highlights the barriers along with drivers of usage and adoption of online food delivery applications. Research Questions for the current inquiry are 1) What dimensions impact the adoption of online food delivery aggregators’ services in Tier 1, 2, and 3 cities? 2) What are the situation triggers, and barriers impacting the usage of online food delivery aggregator’s services in Tier 1, 2, and 3 cities? A qualitative approach is used to collect the data. In-depth interviews are conducted in Tier 1, 2, and 3 cities in India. Users and potential users of online food delivery aggregators were interviewed to study and identify the positive and negative dimensions affecting the usage of online food delivery aggregator apps. Qualitative data analysis is conducted using MAXQDA – qualitative data analysis software. The results of the current study present the factors derived from the qualitative inquiry impacting the adoption and usage of online food delivery apps. The factors are social influence, perceived usefulness, hedonic motivation, Trust, culture, the occasion of adoption, online reviews and ratings, community services, and customization. The analysis also helps to classify the factors into enablers (usefulness, convenience, time and price saving, subjective norms, community services, habit, and app functionality) and barriers (Hygiene, condition of food when delivered, culture, food safety, trust) of usage. The current inquiry helps to understand the dimensions impacting the usage of online food delivery applications in tier1,2 and 3 markets of India. These dimensions can assist the practitioners and developers in app development and understand the usage phenomenon in the specified consumer markets. The current study will help the entrepreneurs of the online food delivery industry to strategies the value proposition based on the consumer insights and requirements.
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Direitos de Autor (c) 2025 New Trends in Qualitative Research

Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição-NãoComercial-SemDerivações 4.0.
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Adams, A. & Cox, A. (2008), Questionnaires, In-Depth Interviews and Focus Groups Book Chapter and Focus Groups, Research Methods for Human-Computer Interaction, Cambridge University Press, Cambridge,17-34
Atulkar, S. & Singh, A., (2021). Role of psychological and technological attributes on customer conversion to use food ordering apps. International Journal of Retail & Distribution Management, 49(10), 1430-1446.
Aiswarya, B., & Ramasundaram, G. (2023). Online food delivery technology and family food traditions: A sociocultural perspective. Journal of Information Technology Teaching Cases, 14(1), 68–73. https://doi.org/10.1177/20438869221150826.
Alalwan, A. A. (2019). 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
Francis, J. J., Johnston, M., Robertson, C., Glidewell, L., Entwistle, V., Eccles, M. P., & Grimshaw, J. M. (2009). What is an adequate sample size? Operationalizing data saturation for theory-based interview studies. Psychology and Health, 25(10), 1229–1245. https://doi.org/10.1080/08870440903194015
Guest, G., Namey, E., & Chen, M. (2020). A simple method to assess and report thematic saturation in qualitative research. PLoS ONE, 15(5), e0232076. https://doi.org/10.1371/journal.pone.0232076
Gupta, V. & Duggal, S., (2020). How the consumer’s attitude and behavioral intentions are influenced: A case of online food delivery applications in India. International Journal of Culture, Tourism and Hospitality Research, 15(1), 77-93.
Hong, C., Choi, H., Choi, E., & Joung, H. (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
India: online food delivery market size 2026 | Statista. (2024, August 30). Statista. https://www.statista.com/statistics/744350/online-food-delivery-market-size-india/
Kaur, P., Dhir, A., Talwar, S., & Ghuman, K. (2021). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33(4), 1129–1159. https://doi.org/10.1108/ijchm-05-2020-0477
Krippendorff, K. (2019). Content Analysis: An Introduction to Its Methodology. https://doi.org/10.4135/9781071878781
Okumus, B. & Bilgihan, A., (2014). Proposing a model to test smartphone users' intention to use smart applications when ordering food in restaurants. Journal of Hospitality and Tourism Technology, 5(1),31-49.
Okumus, B., Ali, F., Bilgihan, A. & Ozturk, A., (2018). Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, 72, 67-77.
Pigatto, G., Machado, J., Negreti, A. & Machado, L., (2017). Have you chosen your request? Analysis of online food delivery companies in Brazil. British Food Journal, 119(3), 639-657.
Phong, N.D., Khoi, N.H. & Nhat-Hanh Le, A. (2018), “Factors affecting mobile shopping: a Vietnamese perspective”, Journal of Asian Business and Economic Studies, 25(2),186-205.
Pandey, S., Chawla, D. & Puri, S., (2021). Food delivery apps (FDAs) in Asia: an exploratory study across India and the Philippines. British Food Journal, 124(3), 657-678.
Roh, M. & Park, K., 2019. Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. International Journal of Information Management, 47, 262-273.
Statista (2022),” Size of the online food delivery market across India from 2020 to 2023, with estimates until 2026”. Available at https://www.statista.com/statistics/744350/online-food-delivery-market-size-india/.
Statista (2019), “India: number of digital buyers 2014-2020”.
Statista (2019), “Online food delivery-India”, available at: https://www.statista.com/outlook/374/119/ online-food-delivery/India
Statista (2019), “Online food delivery-worldwide”, available at: https://www.statista.com/outlook/374/ 100/online-food-delivery/worldwide
Straub, D., Keil, M. & Brenner, W. (1997), “Testing the technology acceptance model across cultures: a three-country study”, Information and Management, 33(1), 1-11
Venkatesh, N., Thong, N., & Xu, N. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6),527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x
Wen, H., Pookulangara, S., & Josiam, B. M. (2021). A comprehensive examination of consumers’ intentions to use food delivery apps. British Food Journal, 124(5), 1737–1754. https://doi.org/10.1108/bfj-06-2021-0655
Yeo, V., Goh, S. & Rezaei, S., 2017. Consumer experiences, attitude, and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35,150-162.
Zhang, J., & Mao, E. (2007), “Understanding the acceptance of mobile SMS advertising among young Chinese consumers”, Psychology and Marketing, 24(9), 763-785
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