A Study on Establishing On-going Concern Prediction Model for Private Junior Colleges, Colleges of Technology,and Universities of Science & Technology in Taiwan

• Main Authors: Liu, Huoo-Chin, 劉火欽
• Other Authors: 馮丹白
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/829pfr

博士 === 國立臺灣師範大學 === 工業教育學系 === 104 === The low birth rate in Taiwan impacts enrollment at all levels of schooling, gradually spreading from primary school to university. The first wave of the low birth rate phenomenon will affect higher education, especially private junior colleges, colleges of technology, and universities of science & Technology (PJCU), in August of 2016. Within the next decade, PJCU, due to the declining birthrate crisis, will face the pressure of decreasing enrollment. Most schools will encounter problems of financial distress and organization decline. How to construct a set of indicators and predictive models to assess financial difficulties and organizational decline are very important for PJCU. The study employed documentary analysis methodology to construct on-going concern indicators. The indicators include on-going concern, financial distress, credit rating theory, the financial system and financial analysis of private schools, organizational decline theory, and criteria students use to choose a school. The study collected financial and non-financial quantitative data from PJCU between 2011 and 2013. The logistic regression analysis and discriminant analysis were employed to construct predictive models. Based on the purpose of this study, and the findings of the study, the conclusions are as follows: 1. This study constructs on-going concern indicators for PJCU, including 10 indicators of first level and 150 indicators of secondary level, to assess the ability of sustainable operation. There are 57 secondary indicators with a significant level of 0.1% between the normal group and the declining group. Such findings could apply to financial analysis for educational authorities and private schools. 2. In the logistic regression analysis model, the most significant indicators are innovative. Therefore, the indicators of this study are of prediction value. 3. The discriminant analysis shows that there are 8 indicators contributing to canonical discriminant function. Among those indicators, 4 of them are relevant to cash-flow aspects. In other words, cash-flow indicators are of more importance to predict the sustainable operation of schools. 4. The correct classification rate of discriminant analysis is higher than that of logistic regression analysis, which means discriminant analysis is a preferable prediction model. 5. The prediction models of logistic regression analysis and discriminant analysis have their own advantages for predicting declining schools. The entire correct classification rate can be enhanced if some indicators employ discriminant analysis prediction models and some indicators employ logistic regression prediction models. 6. There are 48 creative indicators and 4 traditional indicators used within the on-going concern prediction models. The creative indicators are useful for practical application. According to the study, from the documentary analysis and empirical analysis, there are 7 recommendations to educational authorities, 5 proposals for schools, and 5 proposals for follow-up studies.