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This paper aims to distinguish whether the decision-making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit, and highlight significant variables that are crucial in terms of accepting and rejecting applicants, which can further aid the decision-making process.

A real data set of 487 applicants is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models.

The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision-making process.

This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.

The purpose of this paper is to investigate the effect of corporate board attributes, ownership structure and firm-level characteristics on both corporate mandatory and voluntary disclosure behaviour.

Multivariate regression techniques are used to estimate the effect of corporate board and ownership structures on mandatory and voluntary disclosures of a sample of Libyan listed and non-listed firms between 2006 and 2010.

First, the authors find that board size, board composition, the frequency of board meetings and the presence of an audit committee have an impact on the level of corporate disclosure. Second, results indicate that ownership structures have a non-linear effect on the level of corporate disclosure. Finally, the authors document that firm age, liquidity, listing status, industry type and auditor type are positively associated with the level of corporate disclosure.

Future research could investigate disclosure practices using other channels of corporate disclosure media, such as corporate websites. Useful insights may be offered also by future studies by conducting in-depth interviews with corporate managers, directors and owners regarding these issues.

The evidence relating to the important role that corporate governance mechanisms play in shaping the expectations relating to the level of corporate voluntary and/or mandatory disclosures may be useful in informing investor decisions, as well as future policy and regulatory initiatives.

This paper contributes to the existing literature by examining the governance-disclosure nexus relating to both mandatory and voluntary disclosures in both listed and non-listed firms operating in a developing country setting.

This study aims to compare the value relevance of accounting numbers prepared under the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) standards with those produced under the International Financial Reporting Standards (IFRS) for Takaful companies (TC).

The authors assess the value relevance of accounting numbers using the Easton and Harris (1991) and Ohlson (1995) return and price models. They also use 54 insurance companies from 10 developing countries in Asia and the Middle East from 2006 to 2015.

The analysis shows that book value is significantly related to stock price under AAOIFI and IFRS. It also shows that TC adopting AAOIFI accounting standards have a more significant effect on stock price. This suggests that AAOIFI standards are more value relevant than IFRS.

TC and their stakeholders can use the findings to determine which accounting standards (IFRS or AAOIFI) produce the more relevant accounting information. This study is useful for investors that consider Islamic ethical practices to make their investment decisions for the standards-setting bodies that focus on establishing accounting standards for the Takaful industry.

The authors investigate a new aspect of the topic of value relevance. To the best of the authors’ knowledge, they believe this is the first paper examining the value relevance of TC’ accounting information prepared under AAOIFI and IFRS.

The main aims of this paper are: first, to investigate how decisions are currently made within the Egyptian public sector environment; and, second, to determine whether the decision making can be significantly improved through the use of credit scoring models. A subsidiary aim is to analyze the impact of different proportions of sub‐samples of accepted credit applicants on both efficient decision making and the optimal choice of credit scoring techniques.

Following an investigative phase to identify relevant variables in the sector, the research proceeds to an evaluative phase, in which an analysis is undertaken of real data sets (comprising 1,262 applicants), provided by the commercial public sector banks in Egypt. Two types of neural nets are used, and correspondingly two types of conventional techniques are applied. The use of two evaluative measures/criteria: average correct classification (ACC) rate and estimated misclassification cost (EMC) under different misclassification cost (MC) ratios are investigated.

The currently used approach is based on personal judgement. Statistical scoring techniques are shown to provide more efficient classification results than the currently used judgemental techniques. Furthermore, neural net models give better ACC rates, but the optimal choice of techniques depends on the MC ratio. The probabilistic neural net (PNN) is preferred for a lower cost ratio, whilst the multiple discriminant analysis (MDA) is the preferred choice for a higher ratio. Thus, there is a role for MDA as well as neural nets. There is evidence of statistically significant differences between advanced scoring models and conventional models.

Future research could investigate the use of further evaluative measures, such as the area under the ROC curve and GINI coefficient techniques and more statistical techniques, such as genetic and fuzzy programming. The plan is to enlarge the data set.

There is a huge financial benefit from applying these scoring models to Egyptian public sector banks, for at present only judgemental techniques are being applied in credit evaluation processes. Hence, these techniques can be introduced to support the bank credit decision makers.

Thie paper reveals a set of key variables culturally relevant to the Egyptian environment, and provides an evaluation of personal loans in the Egyptian public sector banking environment, in which (to the best of the author's knowledge) no other authors have studied the use of sophisticated statistical credit scoring techniques.

This paper aims to investigate the efficiency and effectiveness of alternative credit‐scoring models for consumer loans in the banking sector. In particular, the focus is upon the financial risks associated with both the efficiency of alternative models in terms of correct classification rates, and their effectiveness in terms of misclassification costs (MCs).

A data set of 630 loan applicants was provided by an Egyptian private bank. A two‐thirds training sample was selected for building the proposed models, leaving a one‐third testing sample to evaluate the predictive ability of the models. In this paper, an investigation is conducted into both neural nets (NNs), such as probabilistic and multi‐layer feed‐forward neural nets, and conventional techniques, such as the weight of evidence measure, discriminant analysis and logistic regression.

The results revealed that a best net search, which selected a multi‐layer feed‐forward net with five nodes, generated both the most efficient classification rate and the most effective MC. In general, NNs gave better average correct classification rates and lower MCs than traditional techniques.

By reducing the financial risks associated with loan defaults, banks can achieve a more effective management of such a crucial component of their operations, namely, the provision of consumer loans.

The use of NNs and conventional techniques in evaluating consumer loans within the Egyptian private banking sector utilizes rigorous techniques in an environment which merits investigation.

The purpose of the paper is to fill gaps in the existing fit and turnover intention (TI) literature by investigating a more comprehensive model, in which TI is proposed to be influenced by the interplays of three multidimensional types of fit including, person-organization (P-O) fit, person-group (P-G) fit, and person-job (P-J) fit.

Participants were selected from different specializations within Mansoura University medical centers, where each medical center was represented proportionately within the sample. Data were collected using self-administered questionnaires. Questionnaires were provided to 850 employees who agreed to participate. Of the 850 questionnaires distributed, 385 were valid and complete (n=385). Partial least squares analysis was utilized for the analyses.

Results showed that P-O fit, P-G fit, and P-J fit were positively related to each other and negatively related to TI. Furthermore, the negative relationship between P-O fit and TI is partially mediated by P-G fit and P-J fit.

The present study simultaneously examines the multidimensional effects of different fit perceptions on TI. In doing so, we identify which of the fit perspectives influence TI more intensely. Moreover, the authors advance current insights by investigating the mediating roles of P-G fit and P-J fit in the relationship between P-O fit and TI.

This study aims to investigate the moderating effect of external financing needs on the relationship between the disclosure level of integrated reporting (IR) and firm value using evidence from Egypt.

This study uses a panel regression analysis for a matched sample of 50 companies listed on the Egyptian Stock Exchange (EGX), specifically from EGX100. The sample covers four years (2017–2020). The current study uses content analysis to measure IR and Tobin’s Q as a proxy for firm value.

The findings reveal a significant positive relationship between the disclosure level of IR and firm value. In addition, the authors find that external financing needs moderate the relationship between IR and firm value. It is concluded that the higher the disclosure level of IR content, the higher the firm’s value, and that this relationship strengthens in firms with high needs for external financing.

Several practical implications can be derived from the results of the current study. Policymakers and regulators can impose mandatory requirements for IR in Egypt. It also opens new insights for board members, managers, analysts and auditors in forming financing decisions based on annual reports.

The present study has a novel insight from a developing country and significant contributions to the extant literature. The study provides empirical evidence from an emerging economy and an insight into how external financing can be used for firms with different levels of IR. It also provides a comprehensive disclosure index to estimate the level of IR.

The unique characteristic of Islamic bank leads in governance and disclosure. Using stakeholder, signaling, and market discipline theory, governance and adequate disclosure may increase bank soundness. This study aims to investigate the relationship of sharia disclosure and Sharia Supervisory Board in influencing Islamic bank soundness in the different regulatory framework of the country. Using purposive sampling, the research covered 84 Islamic banks in 16 countries during the period 2013–2015 with lag data of Islamic bank soundness. The result shows sharia disclosure influences on Islamic bank soundness for management efficiency, capital adequacy ratio, asset quality, and liquidity. The results also show that sharia disclosure mediates the indirect effect of SSB on Islamic bank soundness. The regulatory framework (sharia accounting standard and SSB regulation) shows moderating effect of regulation framework proved on the association of sharia disclosure with management efficiency, capital, and liquidity. The effect is indirectly depending on the regulatory framework for proxy management efficiency, capital, and liquidity. The implication of the research suggests that sharia disclosure could increase the market discipline mechanism of Islamic bank stream. The Islamic bank can increase the transparency using sharia disclosure as a branding for increasing public trust, even though in the deficient Islamic bank regulation countries.

This study aims to identify mutually exclusive risk categories and determine whether these categories effectively capture the potential impact of risk disclosures on the initial returns of initial public offerings (IPOs) in the financial and non-financial sectors.

Data were collected from 131 Indian IPO prospectuses (104 non-financial and 27 financial) issued between 2015 and 2021. Content analysis was performed to identify mutually exclusive risk categories, and the effects of these categories on initial IPO returns were assessed by regression analysis

The findings revealed that risk factor disclosures have a significant impact on underpricing, but not all risk factors are relevant. In the current study, in the financial sector, IPO underpricing was mostly driven by technological and competitive risk factors. In the non-financial sector, underpricing was predominantly influenced by operating risk and compliance risk factors.

The limitations of this study include the use of sentence-based context analysis, which does not assess the quality of risk disclosures. The statistical data reduction technique used to generate mutually exclusive risk categories may also be a limitation.

This research has the potential to assist companies in standardizing the disclosure of risks within IPO prospectuses. The insights gained can inform market regulators in designing policies aimed at aiding investors in formulating investment strategies, ultimately enhancing transparency and clarity regarding information disclosure. Moreover, the findings offer valuable guidance to investors in selecting IPOs aligned with their risk tolerance levels.

From a societal perspective, this study represents advancements by guiding regulators towards developing and regulating standardized, mutually exclusive risk factors. Such measures can aid investors in enhancing their decision-making perspectives regarding IPOs, promoting a more informed and confident investment environment.

This study is a pioneering attempt to address knowledge gaps by identifying distinct categories of risk disclosures in IPO prospectuses and examining their potential influence on IPO underpricing in the financial and non-financial sectors in India.

The absorptive capacity (AC) leads to firm performance and influences the development and evolution of capabilities and routines, but the influence of AC in projects remains unclear. Therefore, this study aims to investigate the effect of AC on project performance (PP) in the construction industry of Sao Paulo State, Brazil.

The authors conducted a survey questionnaire with project managers and collected 157 responses in the construction sector. They also used confirmatory factor analysis (CFA) and multiple linear regression techniques to assess the data.

The study provides empirical evidence that realized absorptive capacity (RAC) has a direct and indirect positive effect on PP. Conversely, the potential absorptive capacity (PAC) only indirectly impacts PP through project management practices (PMPs). PAC and RAC positively influence PMPs that in turn positively influence PP. The findings reinforce the relevance of AC to the development of internal knowledge for processes and routines, thereby enhancing PP.

The findings provide practical implications: the AC influences PP by refining and adapting routines. Moreover, the consistent application of accepted practices is not enough for PP, but the ability to adapt, adjust and transform the relevant knowledge into routines.

This paper provides empirical evidence that the knowledge application of PMPs improves organizational performance through PP. However, despite what the literature has discussed, this paper proved that AC has no effect as a moderating factor between PMPs and performance; however, AC's role significantly impacts PP through PMPs.