The Influential Factors on Fast-food Restaurant: Analyzing from the Perspective of Big Data

Xiaoyi Wang; Feiyang Deng

doi:10.54691/bcpbm.v26i.2059

Authors

Xiaoyi Wang
Feiyang Deng

DOI:

https://doi.org/10.54691/bcpbm.v26i.2059

Keywords:

Big Data; Fast-food Restaurant; customer groups.

Abstract

In recent years, with the development of big data, precision marketing has helped companies improve customer loyalty and increase revenue through the analysis of consumer behavior and preferences. The main purpose of this research is to find the main effectiveness factors on customer groups--college students of fast service restaurants in the United States and offer recommendations based on the analysis for how the business should proceed with its marketing efforts. This paper further develops the Logit model by adding variable data of different dimensions to the model. Grabbed high-frequency words and equation modelling were performed using R studio and linear regression. Findings revealed that college students are the largest proportion of consumers in the quick service restaurants paper investigated. It helps avoid the extensive meaningless promotion of quick service restaurants which gives the restaurants clear direction of strategy improvement.

Downloads

Download data is not yet available.

References

S. Lock, (2021) U.S. quick service restaurant industry market size 2011-2021. https://www.statista.com/statistics/1174417/fast-food-restaurants-industry-market-size-us/

S. Nielsen, A. Siega-Riz and B. Popkin, “Trends in Food Locations and Sources among Adolescents and Young Adults,” Preventive Medicine, Vol. 35, No. 2, 2002, pp. 107-113. doi:10.1006/pmed.2002.1037

L. Harnack and S. French, “Fattening up on Fast Food,” Journal of the American Dietetic Association, Vol. 103, No. 10, 2003, pp. 1296-1297. doi: 10.1016/j.jada.2003.08.029

J. Driskell, Y. Kim and K. Goebel, “Few Differences Found in the Typical Eating and Physical Activity Habits of Lower-Level and Upper-Level University Students,” Journal of the American Dietetic Association, Vol. 105, No. 11, 2005, pp. 798-801. doi: 10.1016/j.jada.2005.02.004

Sasser, W. E., Schlesinger, L. A., & Heskett, J. L. (1997). Service profit chain. Simon and Schuster.

Bowen, J. T., & Chen, S. L. (2001). The relationship between customer loyalty and customer satisfaction. International journal of contemporary hospitality management.

Ladhari, R., Brun, I., & Morales, M. (2008). Determinants of dining satisfaction and post-dining behavioral intentions. International Journal of Hospitality Management, 27(4), 563-573.

Reichheld, F. F., & Sasser, W. E. (1990). Zero defection: Quality comes to service. Harvard Business Review, 68(5), 2-8.

Idemudia, E. C., & Jones, D. R. (2015). An empirical investigation of online banner ads in online marketplaces: the cognitive factors that influence intention to click. International Journal of Information Systems and Management, 1(3), 264-293.

Yin, C., Ding, S., & Wang, J. (2019). Mobile marketing recommendation method based on user location feedback. Human-centric computing and information sciences, 9(1), 1-17.

Brimicombe, A., & Li, Y. (200). Mobile space‐time envelopes for location‐based services. Transactions in GIS, 10(1), 5-23.

Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341-3351.

Bolotaeva, V., & Cata, T. (2010). Marketing opportunities with social networks. Journal of Internet Social Networking and Virtual Communities, 2010, 1-8.