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Previous studies have postulated that an advance payment system (APS) positively impacts the contractor's working capital and is paramount to ensuring an efficient and effective project cash flow process. However, scant research has been undertaken to empirically establish the cash flow performance and domino effect of APS on project and organisational performance.

The epistemological design adopted a positivist philosophical stance augmented by deductive reasoning to explore the phenomena under investigation. Primary quantitative data were collected from 504 Construction Industry Development Board (CIDB) registered contractors (within the grade bandings 1–9) in South Africa. A five-point Likert scale was utilised, and subsequent data accrued were analysed using structural equation modelling (SEM).

Emergent findings reveal that the mandatory use of an APS does not guarantee a positive project cash flow, an improvement in organisational performance or an improvement in project performance.

The ensuing discussion reveals the contributory influence of APS on positive cash flow and organisational performance, although APS implementation alone will not achieve these objectives. Practically, the research accentuates the need for various measures to be concurrently adopted (including APS) towards ensuring a positive project cash flow and improved organisational and project performance.

There is limited empirical research on cash flow performance and the domino effect of APS on project and organisational performance in South Africa, nor indeed, the wider geographical location of Africa as a continent. This study addresses this gap in the prevailing body of knowledge.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.

A mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.

Based on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.

The DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.

The research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

The challenge of accessibility to adequate housing in several countries by a large percentage of citizens has given rise to different housing programs designed to facilitate access to affordable housing. In South Africa, the National Housing Finance Corporation (NHFC) was created to provides housing loans to low- and middle-income earners. Thus, the purpose of this study was to evaluate the implication of the macroeconomic risk elements on the performance of the NHFC incremental housing finance.

This study used a mixed-method approach to examine the time-series data of the NHFC over 17 years (2003–2020), relative to selected macroeconomic indicators. Additionally, this study analysed primary data from a 2022 survey of NHFC Executives.

This study found that incremental housing finance addresses a housing affordability gap, caters to disadvantaged groups, adapts to changing macroeconomic conditions and can mitigate default risk. It also finds that the performance of the NHFC’s incremental housing finance is premised on the behaviour of the macroeconomic elements that drive its strategy in South Africa.

Unlike previous works on housing finance, this case study of the NHFC considers the implication of macroeconomic trends when disbursing incremental housing finance to low- and middle-level income earners as a risk mitigation measure for the South African market. Its mixed method use of quantitative and qualitative data also allows a robust insight into trends that drive investment in incremental housing finance in South Africa.

This paper aims to examine the interdependence of financial decisions (investment, financing, dividends and cash-holding) under financial constraints.

The authors specify and estimate a system of simultaneous equations with panel data and firm fixed effects by three-stage least squares in a sample of firms from 62 countries from 1996 to 2010.

The main findings largely corroborate previous studies regarding the interdependence of financial decisions. The authors also find evidence suggesting that financial constraints have a major impact on firms’ financial decisions. The results also suggest that financial constraints manifest themselves in virtually all firms, indicating that such constraints are a matter of degree and not of kind.

Implications regarding the impact of cash flows on investment and cash-holding decisions are only partially confirmed.

The results are consistent with the hypothesis that financial constraints distort the financial policies of firms. For the purpose of formulating policies that reduce these distortions, the authors emphasize the role of the availability of internal funds and the recoverable fraction of assets in easing financial constraints, thus allowing for greater investment on the part of firms.

The results suggest that regulators should promote policies that reduce the dependence of corporate investment on internally generated cash flows.

Unlike previous studies, the authors account for the direct impact endogenous variables could have on each other. In addition, they explore the impact of each country’s particular legal environment on the pledgeability of assets at the company level.

This study aims to investigate the influence of economic policy uncertainty (EPU) and geopolitical risk (GPR) on corporate cash holding level and speed of adjustment (SOA) in one of the most important emerging markets in the Middle East and North Africa, Saudi Arabia. It also investigates whether Shariah-compliance as well as financial constraints affect the relationship between both EPU and GPR and corporate cash holdings.

The study employs GMM regression considering a sample of 140 nonfinancial firms drawn from the Saudi stock market over the period 2002 to 2019.

The authors find evidence in support of the precautionary motive hypothesis. Facing costly external financing induced by economic policy-related uncertainty and geopolitical tension, Saudi firms tend to accumulate cash as a buffer against negative shocks to their cash flows. The results also show that the positive impact of EPU and GPR on the level of cash holding is less pronounced in Shariah-compliant firms, whereas it is more pronounced in more financially constrained firms. Evidence also reveals that the estimated adjustment coefficients show that Saudi firms adjust more quickly toward their target cash ratio in periods of high economic instability and geopolitical risks.

This study has important implications for managers, policymakers and regulators. For managers, the study is an important reference to understand and design cash management policies by considering factors measured at the country level. More specifically, managers should pay more attention to periods of heightened uncertainties and geopolitical tensions in which the availability of funds is reduced. For policymakers and regulators, this study may be useful in assessing the effect of economic instability on firm’s cash holding decision. Therefore, in an effort to increase the supply of external financing available to firms, policymakers may devise investment friendly environment by controlling country-specific factors.

This paper shows how EPU and GPR as institutional environment factors affect cash holding decision in an oil-rich country.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

This paper explores which fundamental aspects of US insurance firms are significant factors in determining whether a firm will be a target or acquirer firm.

By focusing on 251 mergers and acquisitions (M&A) deals (119 target firms and 132 acquirer firms) over the period between 1990 and 2019, multinomial logistic regression results identify the determinants associated with becoming targets or acquirers.

US insurance firms are more likely to become targets if they are smaller, have lower cash holdings, are non-life, and do not have environmental, social and governance (ESG) scores. Insurance firms are more likely to be acquirers if they have higher profitability, higher cash flow and higher intangibles, and if they are non-life and do not have ESG scores. Moreover, the likelihood of becoming an acquirer decreases in times of global financial crises (GFCs) as compared to non-GFC times.

This paper is the first to utilize multi-period multinomial logistic regression analysis to investigate the determinants of selection decisions of M&A targets and acquirers in the US insurance industry.

Drawing upon underinvestment theory and clientele effect hypothesis, this paper aims to examine the effects of foreign currency (forex) exposure and Shari’ah-compliant status on firms’ financial hedging strategy.

Based on data of 250 nonfinancial firms listed on Bursa Malaysia from 2010 to 2018 (2,250 firm-year observations), the authors test the impact of forex exposure based on a vector of foreign-denominated cash flows (FCF) indicators and firms’ Sharīʿah-compliant status on two proxies of financial hedging decisions, namely, the ratio of the notional value of currency derivatives to total assets and a binomial measure of hedging status. The hedging decision models are estimated using panel logistic regression and system generalized method of moments.

The results indicate significant positive effects of the forex exposure indicators on firms’ propensity to hedge. However, the impact of forex exposure is most prevalent via total FCF. The results also reveal significant positive effects of Sharīʿah-compliant status on firms’ propensity to hedge but its negative impacts on the value of currency derivatives they use. The results suggest that Sharīʿah-compliant firms refrain from engaging in currency derivatives to avoid riba’ and subsequently subdue the clientele effect. However, when the forex exposure reaches higher levels, engagement in currency derivatives becomes a matter of tentative necessity (dharurat).

This study relies exclusively on the disclosure of foreign currency risk and management data in the annual reports of listed companies. Consequently, this limits the sample size to only those nonfinancial listed companies with complete data for the study period. Also, since none of the companies reports using Sharīʿah-compliant derivatives, the authors thus assume that they use derivative instruments that tolerate “riba.”

Given the significance of forex exposure on hedging decisions, the accounting profession must strictly adopt FRS 7 and FRS 139 for all listed firms to avoid market scrutiny and sustain their clientele. The results also call for the Islamic market regulators to include mandatory disclosure of conventional currency derivatives in screening firms for clearly prohibited activities to help enhance the credibility of its Islamic financial market.

Due to difficulty accessing relevant cash flow data, the study is among the few studies that measure forex exposure using FCF and test more proxy indicators. This study is perhaps the first to examine the Shari’ah perspective on currency derivatives in corporate forex risk management.

This paper aims to study the impact of audit quality on the components of executive cash compensation. It is predicted that as audit quality improves, greater emphasis will be placed on the incentive components of cash compensation, and lower emphasis on the salary (fixed) component. Specifically, it is predicted that as audit quality enhances, greater emphasis will be placed on earnings and sales revenues in determining executive cash compensation. Using auditor specialization as a proxy for audit quality, empirical support is provided for all of our predictions.

This paper provides empirical support with agency theoretic predictions.

This paper developed the following hypotheses: H1 – in executive cash compensation, more weight is being placed on earnings-based measures as auditor specialization improves; H2 – in executive cash compensation, more weight is also being placed on sales revenues as auditor specialization improves; H3 – in executive cash compensation, salary levels decrease as auditor specialization improves; and H4 – the impact of auditor specialization on the weight on earnings, sales and the salary levels is lower in the post-Sarbanes–Oxley Act (SOX) period compared to pre-SOX period.

First, the article limits itself to cash compensation, while current executive compensation is largely made of equity. Second, the measure of audit quality used, ‘national level auditor specialization’, may not be as effective in the post-SOX era.

Compensation committees should pay attention to audit quality (in whatever way it may be proxied by) in determining executive compensation.

This is the first paper to show that audit quality not only improves the earnings response coefficient in firm valuation but also enhances the weight placed on earnings (and sales revenues) in executive compensation.