Study on system fairness dimensions and tax compliance in the Middle East context

This study investigates the impact of political instability, macroeconomic and bank-specific factors on the profitability of Islamic banks in the context of Yemen. The study used two common measures of profitability, namely, Return on Assets (ROA) and Return on Equity (ROE) as dependent variables. Seven key independent (internal and external) variables are also used. There are five fully-fledged Islamic banks (IBs) working in Yemen. The study selected only three out of five IBs due to the availability of data for the period ranging from 2010 to 2014. The descriptive and multiple regression analyses were done. The results of the study indicate that operating efficiency and financial risk have negative and significant relationships with ROA and ROE. The findings also show that capital adequacy has a negative and insignificant relationship with ROA and ROE. Furthermore, the study reveals that assets size (LogA), assets management, liquidity and deposits have a significant and positive impact on banks’ profitability. GDP, Inflation rate (IR) and Political instability have positive and significant impact on Yemeni banks’ profitability. Based on the best knowledge of the authors, this study is considered one of the first and pioneering studies that determine the factors affecting the profitability of Islamic banks of Yemen. Therefore, the study gives good insights for the policy makers, regulators and interested parties for enhancing the profitability of Islamic banks in Yemen.

This study seeks to verify the contribution of internal audit (IA), especially its role in improving financial performance in Yemeni commercial banks, with a specific focus on three factors, namely: the independence and objectives of IA, the quality of IA and the size of IA. This study reviews some existing literature on the contribution and role of IA in improving financial performance. It relies on available data from questionnaires. 90 questionnaires were distributed to nine commercial banks in Yemen (23 branches) working under the supervision of the Central Bank of Yemen; 81 questionnaires (90%) were regained and used in the process of analysis. To analyze the data, three analysis approaches were used, including description, correlation, and regression. The results showed that the IA has a significant impact on the overall performance of Yemeni commercial banks. Furthermore, the results showed that the auditors’ efficiencies, as well as their financial and accounting experiences, have a significant and positive impact on financial performance. It was revealed that the independence and objectivity of internal auditors are highly insignificant for financial performance. However, the size of IA and the frequency of the auditors’ meetings have a negative and significant effect on financial performance. This study provides some recommendations for improving the effectiveness of IA, which in turn will contribute to improving financial performance.

Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007–2009 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks’ financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here the authors use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. They also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. The data are collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade of the 21st century. The findings show that when predicting bank FSRs during the period 2007–2009, discriminant analysis is surprisingly superior to all other techniques used in this paper. When only machine learning techniques are used, CHAID outperform other techniques. In addition, the findings highlight that when a random sample is used to predict bank FSRs, CART outperform all other techniques. The evaluation criteria have confirmed the findings and both CART and discriminant analysis are superior to other techniques in predicting bank FSRs. This has implications for Middle Eastern banks, as the authors would suggest that improving their bank FSR can improve their presence in the market.