Saudi Arabia is attempting to structurally transform itself to move away from its dependence on oil. Towards this, its education sector plays an important role. It provides the economy with the needed graduates. It is highly desirable that the education sector of the country attains and maintains quality standards. This study identifies the three dimensions of teaching staff, administrative staff and systems towards the Total Quality Management in a University setting. The study finds a significant difference between the sample respondents over the satisfaction with the faculty, staff, and systems. Further, the study uses the method of logistic regression to analyze the responses of University students’ perspectives of quality. The results indicate that the odds of being satisfied with the University are the highest if the students are satisfied with the systems. It followed by faculty and then the non-teaching staff. Improving the quality of faculty, staff, and systems in education services is needed to achieve the structural transformation desired by the country.

This study aims to examine the influence of credit risk and capital adequacy of a rural bank on financial distress, proxied by interest coverage ratio (ICR). Samples used in this research are 123 rural banks located in the Jakarta metropolitan area from 2013 to 2018. In this area, almost 70% of cash flow circulation in Indonesia was happening. The logistic regression model was employed to analyze the collected data. The findings show that both credit risk and capital adequacy had significant influences on financial distress, with positive and negative effects, respectively. Realizing the important role of credit risk and capital adequacy, this study makes some suggestions that rural banks should utilize both variables as a measure to monitor their financial performance.

The purpose of this study is to analyze the demographic and personality characteristics toward the level of student's financial literacy. We use gender, age, parental income, pocket money, and place of residence as the proxies of demographic characteristics. In addition, financial attitude, and financial behavior are considered as proxies of personality characteristics. The population of this research is the students of business/management department of Universitas Negeri Jakarta. We use 194 students as the sample which represents twenty percent of population. The result of this study shows that the level of financial literacy of the students is in middle category. We use logistic regression as the statistical tool and also found that several proxies like age, pocket money, and financial behavior had positive effects toward the level of student's financial literacy. However, other proxies like residence and financial attitude had a negative sign on financial literacy. While gender and parental income had no significant effect on financial literacy.

This paper measures the likelihood of bankruptcy based on some financial ratios such as debt ratio, current ratio, sales to total assets, etc. using logistic regression and classification and regression tree techniques (CART). The proposed study gathers the information of 36 bankrupted firms and 36 non-bankrupted firms based on Tobin Q ratio. The study gathers the necessary information over the period 2005-2011. The preliminary results indicate that both methods have the capability to predict bankruptcy but with different accuracy. The likelihood of bankruptcy based on CART method were 97.2, 97.5 and 86.1 in the event of bankruptcy, one year before and two years prior to bankruptcy, respectively. These figures were changes to 98.6, 94.4 and 84.7 based on logistic method.

One of the primary concerns in most financial institutions to have an appropriate method for ranking customers. Bank customers are the primary sources of creating income and the success of banking industry depends on how to select good customers for allocation of loans. This paper uses Decision Tree, K-nearest neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and Logistic Regression for data categorization to estimate credit ranking of bank customers in one of major banks in Middle East. The results indicate that Logistic Regression was considered as the best method for ranking customers with the precision of 76.17% while Decision Tree was considered as the weakest technique with the precision of 73.30%.