바로가기메뉴

본문 바로가기 주메뉴 바로가기

ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

logo

  • P-ISSN1738-3110
  • E-ISSN2093-7717
  • SCOPUS, ESCI

외식프랜차이즈기업 부실예측모형 예측력 평가

Evaluating Distress Prediction Models for Food Service Franchise Industry

The Journal of Distribution Science(JDS) / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2019, v.17 no.11, pp.73-79
https://doi.org/https://doi.org/10.15722/jds.17.11.201911.73
김시중 (The Graduate School of C-MBA, Woosong University)

Abstract

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

keywords
Distribution Channel System, Food Service Franchise, Distress Prediction Model, Discriminant Analysis, Logit Analysis

참고문헌

1.

Altman, E. I. (1968). Altman, financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.

2.

An, S. J., Shin, C. S., & Park, D. S. (2019). Comparison of restaurant distribution entrepreneurs’ pressure on business failure and entrepreneurial intention. Journal of Distribution Science, 17(5), 5-17.

3.

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4(3), 71-111.

4.

Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 16(Spring), 167-179.

5.

Kim, H. S., Choi, Y. S., & Shin, C. S. (2019). Relationship among restaurant owner’s SNS marketing, trust, purchase intention, and word of mouth intention. Journal of Distribution Science, 17(7), 27-38.

6.

Laitinen, E. K., & Suvas, A. (2016). Financial distress prediction in an international context: Moderating effects of Hofstede’s Original Cultural Dimensions. Journal of Behavioral and Experimental Finance, 9(March), 98-118.

7.

Lee, B. H., & Lee, S. H. (2018). A study on financial ratio and prediction of financial distress in financial markets. Journal of Distribution Science, 16(11), 21-27.

8.

Lee, J. W., & Kwag, M. (2017). Corporate marketing strategy using social media: A case study of the RitzCarlton Seoul. Journal of Asian Finance, Economics and Business, 4(1), 79-86.

9.

Li, H. L., & Sun, J. (2011). Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. Expert Systems with Applications, 38(5), 6244-6253.

10.

López Iturriaga, F. J., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42(6), 2857-2869.

11.

Luc, P. T. (2018). The relationship between perceived access to finance and social entrepreneurship intentions among university students in Vietnam. Journal of Asian Finance, Economics and Business, 5(1), 63-72.

12.

Muhammad, H., Rehman, A. U., & Waqas, M. (2016). The relationship between working capital management and profitability: A case study of tobacco industry of Pakistan. Journal of Asian Finance, Economics and Business, 3(2), 13-20.

13.

Ohlson, J. A. (1980). Financial ratios and the probability prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.

The Journal of Distribution Science(JDS)