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The purpose of this study is to investigate the effect of financial performance (FP) and governance on the accounting and auditing organization for Islamic financial institutions (AAOIFI) disclosure for the Islamic banks.

The authors used the generalized least squares (GLS) estimation for 47 MENASA (Middle East, North Africa and Southeast Asia) Islamic banks (IBs) between 2012 and 2019. In this regression, disclosure is the endogenous variable. The performance and governance measures are the explanatory parameters. The authors use bank's size, leverage and age for control parameters. The robustness of results is verified via generalized method of moments (GMM) estimation method.

The findings indicate that performance measurement has weak effects on AAOIFI disclosure. Only the net interest margin (NIM) measure has a significant positive impact. The return of assets (ROA) and the return on equity (ROE) have a significant negative impact. Furthermore, all Shariah Governance measures have significant effects. Finally, the findings of this study support the governance's positive contribution to the disclosure in IBs.

Through including the whole issues allied to AAOIFI and their impacts on the banks' value, this study provides a significant summary for IBs, policymakers, regulators, AAOIFI and connected authorities across countries. In addition, the findings linked powers between jurisdictions with recommendations on growing the present AAOIFI practices.

This paper offers an original contribution to the accounting professionals and stakeholders who investigate the relationship between disclosure, performance and governance. It is considered as a basis for future studies in the simultaneous relation between these variables. It is crucial for accounting professionals, researchers and stakeholders interesting in the financial disclosure (FD) in IBs.

This study aims at investigating the impact of the disclosure and the Shariah governance on the financial performance in MENASA (Middle East, North Africa and Southeast Asia) Islamic banks.

We use the Generalized Least Squares (GLS) regression models to check the interdependence relationship between the disclosure, the Shariah governance and the financial performance of 47 Islamic banks (IBs) from ten countries operating in MENASA region. The sample period is from 2012 to 2019. In these regressions models, Return on Assets (ROA) and Return on Equity (ROE) are the dependent variables. The disclosure and the Shariah governance indicators are the independent factors. To measure the Shariah governance, we use the three sub-indices, which are the Board of Directors (BOD), the Audit Committee (AC) and the Shariah Supervisory Board (SSB). Size, Leverage and Age of the bank are used as control variables. We also used The Generalized Method of Moments (GMM) and the three-stage least squares (3SLS) estimations for robustness check.

Result shows a negative relationship between the disclosure and the two performance measures in IBs. Furthermore, as far as the governance indicators are concerned, we found that the BOD and AC, as well as the BOD and SSB, have a positive and significant impact on the ROA and ROE, respectively. This reveals that good governance had a significant association with higher performance in MENASA IBs.

The paper considers both IBs that adopt mandatory as well as voluntary AAOIFI standards and the GLS method to investigate the impact of the AAOIFI disclosure and the Shariah governance on ROA and ROE. Also, it uses the GMM and the 3SLS estimations for robustness check. It is relevant for researchers, policymakers and stakeholders concerned with IBs' performance.

Corporate social responsibility (CSR) and fintech have emerged as critical megatrends in the banking industry. This study aims to examine the impact of financial technology on the relationship between CSR and banks' financial stability. Specifically, it investigates the moderating effect of fintech on the association between CSR and the financial stability of conventional banks operating in Qatar, UAE, Saudi Arabia, Kuwait, Bahrain, Jordan, Pakistan and Turkey from 2010 to 2021.

To achieve the authors’ objective, the authors apply Baron and Kenny's three-link model, tested with fixed and random effects regression models.

The results reveal that the development of fintech decreases banks' financial stability, whereas it promotes banks' involvement in CSR strategies. Furthermore, the findings indicate that fintech plays a moderating role in the relationship between CSR and financial stability. It positively moderates the impact of CSR on financial stability. The robustness analysis highlights the mutual reinforcement of fintech and CSR dimensions in improving the financial stability of banks. Thus, by fostering community and product responsibility, fintech could enhance the financial stability of banks.

Finally, the authors recommend that banks focus more on developing technological and environmentally friendly financial products.

This study contributes significantly by providing valuable insights for managers and policymakers seeking to improve banks' financial stability through the simultaneous adoption of new financial technology products and the strong commitment to CSR practices.

This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals.

After preprocessing textual data, the original Neutrosophic TF-IDF applies the neutrosophic inference system (NIS) to produce weights for terms representing a document. Using the local frequency TF, global frequency IDF and text N's length as NIS inputs, this study generate two neutrosophic weights for a given term. The first measure provides information on the relevance degree for a word, and the second one represents their ambiguity degree. Next, the Zhang combination function is applied to combine neutrosophic weights outputs and present the final term weight, inserted in the document's representative vector. To analyze the NTF-IDF impact on the classification phase, this study uses a set of ML algorithms.

Practicing the neutrosophic logic (NL) characteristics, the authors have been able to study the ambiguity of the terms and their degree of relevance to represent a document. NL's choice has proven its effectiveness in defining significant text vectorization weights, especially for text classification tasks. The experimentation part demonstrates that the new method positively impacts the categorization. Moreover, the adopted system's recognition rate is higher than 91%, an accuracy score not attained using the FTF-IDF. Also, using benchmarked data sets, in different text mining fields, and many ML classifiers, i.e. SVM and Feed-Forward Network, and applying the proposed term scores NTF-IDF improves the accuracy by 10%.

The novelty of this paper lies in two aspects. First, a new term weighting method, which uses the term frequencies as components to define the relevance and the ambiguity of term; second, the application of NL to infer weights is considered as an original model in this paper, which also aims to correct the shortcomings of the FTF-IDF which uses fuzzy logic and its drawbacks. The introduced technique was combined with different ML models to improve the accuracy and relevance of the obtained feature vectors to fed the classification mechanism.