Liquidity creation by Islamic and conventional banks during the Covid-19 pandemic.

This study investigates how liquidity has been created during the Covid-19 pandemic and examines the different behaviours to create liquidity for different types of bank ownership. Monthly panel data of 85 Indonesian Islamic and conventional banks are utilized to capture the time periods before and during the pandemic. The estimated regression model applies one period lag for all independent variables to reduce any endogeneity problems. The results indicate that overall banks created less liquidity during the pandemic. This finding implies that adding assets is not an effective strategy to create liquidity since banks transferred assets into safer investments during the pandemic. However, Islamic banks created more liquidity, especially when off-balance sheet activities are not included in the liquidity measurement. This phenomenon validates the unique market structure of Islamic banks, which must comply with Sharia law and avoid holding and trading prohibited assets. On the liability side, the deposits of these banks will be more liquid due to their depositors' religiosity and risk preferences. This study further indicates that the government-owned bank generated more liquidity than other ownership types. These results imply that the government successfully rescued the economy during the pandemic through the use of economic stimuli.

Conditional Conservatism in Islamic Banks During the COVID-19 Pandemic

This study explores conditional conservatism (CC) in listed Islamic banks (IB) during the COVID-19 pandemic. The author collects data manually over the period from 2019 to 2020. In order to capture CC, the author uses the C_score measurement in the main model. As predicted, the author finds an increase in CC level within IB during the COVID-19 pandemic. This finding enriches the current literature on CC, IB, and the economic outcomes of the COVID-19 pandemic. On the other hand, decision-makers (investors, creditors, etc.) can benefit from the governance role of CC experienced in IB during crises such as the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Indonesian Islamic banks: A review of the financial state before and after the COVID-19 pandemic

Banking plays an important role in business and economic growth. However, since a couple decades ago, there have been issues with efficiency and performance. This paper aims to examine Indonesia’s Islamic banking performance through non-parametric production efficiency analysis before and after the COVID-19 pandemic, 2010–2021. This study differentiated between different dimensions of Indonesia’s Islamic banks (IIB) finance and non-finance aspects, as well as investigated the relationships between these dimensions of finance, including assets, deposits, equity, financing, and income, and non-financial variables, namely employees and offices. Non-parametric analysis, with the input-oriented variable constant return to scale (CRS) and returns to scale (VRS) models as a framework, data envelopment analysis (DEA) is used to calculate the IIB of overall, pure, and scale efficiency. However, the resources of technology IIB management are lacking, as well as macroeconomic and environmental effects. This study found that IIB operational needs to enhance investment in technology beyond the office. This means that the number of offices has a smaller impact on enhancing deposits and revenue. Technology investment has a crucial role in enhancing IIB equity, income, and innovation service. As a result, IIB managers and policymakers must improve their efficiency scores in order to increase competition and innovation. Furthermore, IIB needs to increase and spend their assets and experience to enhance technology, which significantly affects efficiency.

Social Reporting by Islamic Banks: The Role of Sharia Supervisory Board and the Effect on Firm Performance

This study aims to explore social reporting by Islamic banks (IB) (referred to as Islamic social reporting, ISR, hereafter) through two streams, i.e., its determinants and consequences on firm performance. Using annual report data from 90 samples of the world’s IB from 2016–2020, this study focuses on the sharia governance implementation through the role of the Sharia Supervisory Board (SSB). The SSB was measured by individual characteristics and IG-Score, representing a combination of dichotomous characteristics of the SSB, which have not been encountered in previous studies. Firm performance as a consequence of disclosure was determined by a more comprehensive approach based on accounting and the stock market. The study’s findings demonstrate the SSB’s beneficial influence on ISR, suggesting that the presence of an SSB can promote ISR practices. Social reporting has been found to have a negative impact on ROA, but it has a positive impact on MTBV and Tobin’s Q. The data suggest that while voluntary reporting practices may cause a short-term decline in profitability, they can have a positive impact on an enterprise’s long-term value.

Islamic Banks Disclosure Tone Prior and during COVID-19

COVID-19 pandemic affects the global economic condition starting from the end of 2019. The narrative section of the firm's annual reports was found to have an association with a firm performance from the previous financial crisis. For that purpose, the current paper studied the changes in the Islamic reported tone prior to and during the COVID-19 period from DICTION 7.0 master tones: activity, optimism, certainty, realism, and commonality tones. © 2021 IEEE.

Herding behaviour in the Islamic bank market: evidence from the Gulf region

Purpose: This study examines herding in Islamic bank equity markets under various market conditions (up/down, high/low trading and high/low volatility) and during events such as Organization of the Petroleum Exporting Countries (OPEC) meeting days, Ramadan, the Gulf Cooperation Council (GCC) crisis of 2017 and the COVID-19 pandemic. The authors also look at the impact of rising and falling oil prices on herding behaviour. Design/methodology/approach: This study uses the model of Chang et al. (2000) to estimate herding behaviour in the Islamic bank markets. Findings: First, the authors estimate herding at the GCC region level, and the results reveal an absence of herding under all market conditions and during all the events considered, except for the GCC crisis of 2017. Second, the authors investigate herding in four Gulf countries (Saudi Arabia, United Arab Emirates [UAE], Qatar and Kuwait) separately and find that herding is evident in all these countries during various market conditions. During Ramadan, herding appears in the Saudi Arabia and Kuwait Islamic bank equity markets. Herding is not prevalent during OPEC meeting days in any of the markets, whereas herding is evident in Saudi Arabia, UAE and Kuwait Islamic bank equity markets during the GCC crisis of 2017 and the COVID-19 pandemic. Lastly, the rising and falling oil prices do not influence herding at either GCC region or country level. Practical implications: From the practitioner's perspective, this study provides useful insights for investors in Islamic banks and policymakers, in terms of asset pricing, portfolio diversification, trading strategies and market stability. Originality/value: Many studies explore herding in the equity markets of Muslim majority countries, but not specifically in the Islamic bank market. This study fills this literature gap by comprehensively examining herding in Islamic bank equity markets under various market conditions (up/down, high/low trading and high/low volatility) and during events, such as OPEC meeting days, Ramadan, the GCC crisis of 2017 and the COVID-19 pandemic. © 2022, Emerald Publishing Limited.

Assessing the effect of organisational commitment on turnover intentions amongst Islamic bank employees

Purpose: The research aims to test the links amongst Meyer and Allen's three levels of organisational commitment and the commitment's effect on reducing turnover intentions for Islamic bank (IB) employees during the lockdown caused by coronavirus disease (COVID-19). Design/methodology/approach: The research follows a variable-centred approach. Primary data are collected through a survey of 324 respondents comprising IB employees from three Arab countries, notably the United Arab Emirates (UAE), Lebanon and Oman. Exploratory factor analysis (EFA) and Cronbach's alpha test are conducted to test the construct validity, reliability and internal consistency of collected data. Descriptive statistics are used to interpret the data. Zero-order correlations, multiple regression analysis and Fisher's Z-test are applied to assess the interrelations of the various groups of variables and the determinants of turnover intentions. Findings: Results show that there is a high level of significant intercorrelation amongst affective, normative and continuance commitments as well as amongst organisational commitment, individual differences and turnover intentions for IB employees from the three studied Arab countries. The results confirmed that turnover intentions are minimised in the presence of all three organisational commitment subscales and that individual differences amongst IB employees and organisational efficiency moderate the relationship between organisational commitment and turnover intentions. Originality/value: There is no empirical work that has been done on the determinants of turnover intentions amongst IB employees during the lockdown. This is valuable to organisational behaviour scholars and practitioners who are interested in the role that organisational commitment plays in IB's employment behaviour. © 2021, Carole Serhan, Nehmeh Nehmeh and Ibrahim Sioufi.

Classification of non-performing financing using logistic regression and synthetic minority over-sampling technique-nominal continuous (SMOTE-NC)

Financing analysis is the process of analyzing the ability of bank customers to pay installments to minimize the risk of a customer not paying installments, which is also called Non-Performing Financing (NPF). In 2020 the NPF ratio at one of the Islamic banks in Indonesia increased due to the decline in people’s income during the Covid-19 pandemic. This phenomenon has led to bad banking performance. In December 2020 the percentage of NPF was 17%. The imbalance between the number of good-financing and NPF customers has resulted in poor classification accuracy results. Therefore, this study classifies NPF customers using the Logistic Regression and Synthetic Minority Over-sampling Technique Nominal Continuous (SMOTE-NC) method. The results of this study indicate that the logistic regression with SMOTE-NC model is the best model for the classification of NPF customers compared to the logistic regression method without SMOTE-NC. The variables that have a significant effect are financing period, type of use, type of collateral, and occupation. The logistic regression with SMOTE-NC can handle the imbalanced dataset and increase the specificity when using logistic regression without SMOTE-NC from 0.04 to 0.21, with an accuracy of 0.81, sensitivity of 0.94, and precision of 0.86. © Al-Zaytoonah University of Jordan (ZUJ).