Introduction
The conceptual framework of this work is to test the experience of using food delivery applications, technical issues, and user attachment. Such a study is like determining consumer behavior in the context of this issue (Gordon, Jorge, and Rafael, 2021). It can be helpful for the creators of these applications as information for marketing departments.
Data Analysis
The survey provided critical primary data on food delivery app usage habits and trends. Answers to questions received from 48 respondents make it possible to apply various statistical analysis methods: from testing various hypotheses of the correlation between the measured values to extrapolating the results of the sample to the general population. In this paper, we have analyzed the means, which allows us to interpret the survey responses most simply, without being tied to a specific hypothesis (Shrestha, 2021).
In addition, it is crucial to consider the survey’s target audience, which makes up the majority of the sample, so that when constructing confidence intervals or extrapolating the study, it is possible to create proportions or strata (Cobern & Adams, 2020). For this, graphs of the demographic data collected during the survey were built, as shown in Figure 1. An analysis of the averages in the form of a histogram is shown in Figure 2.
Conclusion
The analysis showed that most respondents use food delivery apps sometimes or frequently, and they are generally satisfied with the use or do not feel much about it. However, users are willing to recommend these applications to their friends much more often, even though a minority use them for daily meals. It is worth noting that the most critical factor is the speed of delivery that applications offer, while the cost of error for companies is relatively high; users will almost certainly switch to competitors in case of failure. In addition to speed, users appreciate order accuracy and the variety of menus available. Clients are ready to try new applications only if frequent errors are detected with the current one.
Reference List
Cobern, W., and Adams, B. (2020) ‘Establishing survey validity: A practical guide’, International Journal of Assessment Tools in Education, 7(3), pp. 404-419. Web.
Gordon R, F., Jorge M, O. C., and Rafael B, P. (2021) ‘Consumer behavior analysis and the marketing firm: measures of performance’, Journal of Organizational Behavior Management, 41(2), pp. 97-123. Web.
Shrestha, N. (2021) ‘Factor analysis as a tool for survey analysis’, American Journal of Applied Mathematics and Statistics, 9(1), pp. 4-11. Web.