This paper aims to identify the determinants and predictors of Small and Medium-sized Enterprises (SMEs)’ financial failure. Within this framework, we have opted for a quantitative method based on a sample of healthy and failing SMEs of a Moroccan bank. The main results of the different optimal models are obtained by the stepwise method of estimating logistic regression. These results show, in a normal economic context, that the variables that discriminate between healthy and failing SMEs are the main predictors of financial failure. Autonomy ratio, interest to sales, asset turnover, days in accounts receivable, and duration of trade payables are the variables that increase the probability of financial failure, while repayment capacity and return on assets reduce the probability of failure. These variables present an overall classification rate of healthy and failing SMEs of 91.11% three years before failure and of 84.44% two years and one year before failure.

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.

This study provides empirical evidence of tax-induced profit-shifting by multinational corporations (MNCs) operating in Morocco, an underexplored developing country context characterized by notable tax arbitrage potential. Using a micro-level panel dataset of foreign-owned subsidiaries from 2014 to 2023, we employ a pseudo-ordinary least squares (POLS) framework to examine how corporate income tax (CIT) differentials affect subsidiaries’ earnings before interest and taxes (EBIT). The results indicate that higher CIT differentials significantly reduce reported profits, supporting the indirect evidence on corporate profit-shifting behaviour. Our findings also document that the effect of the CIT differential on EBIT is moderated by firm capitalization. However, contrary to investment distortion theory, subsidiaries do not reduce investment in response to higher effective capital costs. This study also assesses the impact of Morocco’s implementation of BEPS, the COVID-19 shock, and institutional quality indicators on subsidiaries’ reported EBIT. The findings highlight the strategic role of capital structure and governance in shaping MNCs’ tax-motivated behaviour. This study contributes to the literature on international taxation and corporate finance and offers important policy implications for developing economies seeking to balance revenue integrity, investment incentives, and robust anti-avoidance enforcement.

This article analyzes the extent to which Moroccan banks adopt a holistic approach to credit risk assessment, incorporating criteria that go beyond simple balance sheets and financial ratios. To this end, Multiple Correspondence Analysis and advanced statistical techniques were employed using R software. A detailed questionnaire comprising 14 questions, was distributed to a sample of Moroccan bankers. The results of this analysis reliably confirm our initial hypotheses. They reveal a growing trend among Moroccan banks towards a more complex and nuanced assessment of credit risk. In addition to traditional financial aspects such as financial statement analysis, banks are increasingly considering non-financial criteria such as collateral requirements, quality of corporate governance, sustainability of business practices, and technological positioning. This diversified approach gives banks a more accurate and comprehensive view of a company's risk profile. It also enables them to make better-informed and more balanced lending decisions, considering the multitude of factors that can influence a company's ability to repay.

Although project management (PM) literature has addressed project success, attention has not been paid to statistically testing the relationship between various critical success factors (CSFs) and project success in developing countries. Determining the most relevant factors can optimize efforts and boost the project success dimension in this specific environment that struggles to achieve the desired development objectives. Therefore, the aim of this paper is to statistically prove the relationship between project success and an extant range of selected CSFs that englobe PM variables, organization structure, project manager competencies, communication, top management, risk management, and prioritize them to provide valuable information to project managers and enhance the likelihood of project success. The data were collected by means of a survey addressed to project managers and practitioners from Morocco in both public and private sector. Multiple stepwise regressions were conducted to uncover the relationship between each success criteria and the considered CSFs. The results show that the considered model for the first factor of project success covers 59,1% of its variance. The significant variables of this model are PM competencies and PM KPI that increase the success of the first factor by 49% and 29%, respectively. The variance of the second factor of project success is explained by 68%. The significant independent variables are communication, top management support, PM Partnership, PM KPI, and PM policy and strategy. According to the results, the variables participate in project success by 37%, 28,1%, 19,8% 29,9% and 15,5%, respectively.