Factor Influencing Economic Growth Of Online Food Ordering And Delivery Application During Covid-19

Document Type : Primary Research paper

Authors

1 HOD, Department of Electronics and Communication Engineering, Karavali College of Engineering, Mangalore, 575013.

2 Student, Nutrigenomics, Symbiosis Institute for Health Sciences, Sus-Pashan Road, Pune-412115.

3 Professor, Department of Computer Science and Engineering, CMR Institute of Technology, kandlakoya, Medchal, Hyderabad, 501401.

4 Assistant Professor, Department of Plant Pathology, G. H. Raisoni University, School of Agriculture Science, Saikheda, Village - dhoda borganv, Tah-Sauser, Dist. - Chindwada, Madhya Pradesh-480337.

5 Director, Business Management, RBVRR Women's College , Narayanguda ,500027.

6 Assistant Deputy Director (Graphic Design), Shri Vishwakarma Skill University,plot no 147,sector 44, Gurugram,122003.

Abstract

Online food delivery (OFD) apps have grown in popularity in recent years, making it easier for customers to shop through online channels due to the convenience they provide. Lockdown was employed in India during the current COVID-19 pandemic in order to restrict the spread of illness.As a result of recent corporate scandals, demand for this speciality has skyrocketed. Consumers, on the other hand, are at danger when it comes to OFD. It improved consumer cleanliness and altered consumer perceptions of OFD. Because earlier research operations are no longer relevant due to constant changes in consumer behaviour, elements influencing consumer decision-making in the area of OFD should be investigated again and again.The research's main goal is to use exploration factory analysis to find the characteristics that influence OFD selection during lockout to India, allowing OFD service providers to make strategic decisions based on customer desired value. Analysis The major component analysis is used to collect the smallest fraction that contributes to the maximum number of variables specified using a sample of 215 models with a set of twenty variables. Three variables were eliminated from the analysis because they did not adequately characterise the variables of the component. Using a linear combination of actual variables, it generates a set of unrelated hidden variables.The study came to a conclusion with seventeen characteristics classified into four categories: efficiency, information, control, safety, quality, and cleanliness.

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 1
International virtual conference on Newer Trends and Innovation in Nanotechnology Materials Science . Science and Technology
March 2021
Pages 242-249

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