Food Delivery Company Market Share Model

Main Article Content

Supot Seebut

Abstract

The objective of this research is to present mathematical modeling of the market share of food delivery companies using a system of a difference equation. It starts from studying the analytical model in Markov chain form and then converts it into a system of a difference equation. To determine the parameters of the model, 121 students were randomly assigned to answer a questionnaire on service return and service satisfaction of each company. Both of these data are considered when calculating the parameters of the model. A python program was used to calculate the model's solution to analyze the long-term market share behavior of a food delivery company. According to the terms of the market share, 3 different conditions, it was found that over the long term, the market share will converge to the same constant ratio in all three cases. The result will be mathematical information that is useful for food delivery companies to be prepared to manage both in terms of market and service.

Article Details

How to Cite
Seebut, S. (2021). Food Delivery Company Market Share Model. Journal of Science and Science Education (JSSE), 4(2), 164–171. retrieved from https://so04.tci-thaijo.org/index.php/JSSE/article/view/253880
Section
Research Articles in Science

References

Albright, B. and Fox, W. P. (2019). Mathematical modeling with excel. CRC Press.

Andrieu, C., Doucet, A. and Holenstein, R. (2010). Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3), 269-342.

Bidabad, B. and Bidabad, B. (2019). Complex probability and Markov stochastic process. Indian Journal of Finance and Banking, 3(1), 13-22.

Fox, W. P. (2011). Mathematical modeling with Maple. Nelson Education.

Giordano, F. R., Fox, W. P. and Horton, S. B. (2013). A first course in mathematical modeling. Nelson Education.

Kassa, A. M., Abrham, E. and Seid, T. (2017). Application of markov chain analysis model for predicting monthly market share of restaurants. International Journal of Recent Engineering Research and Development, 2(30), 48-55.

Myers, D., Wallin, L. and Wikström, P. (2017). An introduction to Markov chains and their applications within finance. Retrieved 21 March 2019, from Department of Mathematical Sciences, Chalmers University of Technology: http://www.math.chalmers.se/Stat/Grundutb/CTH/ mve220/1617/redingprojects16-17/IntroMarkovChainsandApplications.pdf.

Seebut, S. (2020). Mathematical modeling of stock market states using the system of a difference equation. Journal of Science and Science Education, 3(2), 200-208.

Svoboda, M. and Lukas, L. (2012). Application of Markov chain analysis to trend prediction of stock indices. In Proceedings of 30th international conference mathematical methods in economics (pp. 848-853). Karviná: Silesian University, School of Business Administration.

Vasanthi, S., Subha, M. V. and Nambi, S. T. (2011). An empirical study on stock index trend prediction using markov chain analysis. Journal of Banking Financial Services and Insurance Research, 1(1), 72-91.

Yu, K. and Sato, T. (2019). Modeling and analysis of error process in 5G wireless communication using two-state Markov chain. IEEE Access, 7, 26391-26401.

Zhang, D. and Zhang, X. (2009). Study on forecasting the stock market trend based on stochastic analysis method. International Journal of Business and Management, 4(6), 163-170.