Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

Based on the resource-based view and institutional theory, this study investigates the impact of their environmental management capabilities and environmental, social, and governance (ESG) pressure on the non-financial performance of small and medium-sized enterprises (SMEs). In particular, it examines the interaction effect of ESG pressures on the relationship between SMEs’ environmental management capabilities and non-financial performance. For this study, a total of 1865 SME lists were obtained through Jeonnam Techno Park and Jeonnam Small Business Job and Economy Promotion Agency. Based on this, a total of 127 questionnaires were returned as a result of a telephone, e-mail, and online survey, and finally, an empirical analysis was conducted based on 120 questionnaires. We conducted an empirical analysis of Korean SMEs and obtained the following results: First, environmental management capabilities have a significant, positive effect on SMEs’ non-financial performance. Second, ESG pressure has a significant, negative effect on the non-financial performance of SMEs. Next, we analyzed the moderating effect of ESG pressures and observed that ESG pressures strengthen the positive effect of environmental management capabilities on non-financial performance. Based on the resource-based perspective and institutional theory, this study provides meaningful academic implications by examining environmental management capabilities and ESG pressures, which have not been identified in previous studies, as factors of non-financial performance that are becoming important under the new management paradigm, such as climate change and ESG. Furthermore, while ESG pressure has a significant negative effect on non-financial performance, we find that it is a moderating variable that strengthens the relationship between SMEs’ environmental management capabilities and non-financial performance, which has useful academic and practical implications for ESG and strategic management.

This study explores the interconnected roles of organizational atmosphere, psychological capital, work engagement, and psychological contract on the work performance. Structural equation modeling and moderated mediation analyses were conducted to test the hypothesized relationships. Methodologically, the study employed a stratified random sampling of 369 faculty members across various disciplines. Key findings reveal that both organizational atmosphere and psychological capital have a significant positive impact on work engagement, which in turn, enhances work performance. Work engagement acted as a mediator in these relationships. Moreover, the psychological contract was found to moderate the relationship between work engagement and work performance, indicating that the engagement-performance link is stronger when employees perceive their psychological contract has been fulfilled. The implications of this research are multifaceted. Theoretically, it contributes to organizational behavior literature by integrating psychological contracts into the engagement-performance narrative. Practically, it provides actionable insights for university administrators, suggesting that investments in a supportive organizational atmosphere and the development of faculty psychological capital are likely to yield improvements in engagement and performance. The study also underscores the importance of effectively managing psychological contracts to maximize employee output.

This paper aims to investigate the determinants of performance for insurance companies in Tunisia from 2004 to 2017. Namely, we consider three dimensions of determinants; those related to firms’ microenvironment, macroenvironment and meso or industry environment. The performance of insurance companies is measured using three criteria: Return On Assets (ROA), Return On Equity (ROE), and Combined Ratio. The independent variables are categorized into three groups: microeconomic variables (Firm Size, Financial leverage, Capital management risk, Volume of capital, and Age of the firm), meso-economic variables (Concentration ratio and Insurance Sector Size), and macroeconomic variables (Inflation, Unemployment, and Population Growth). The General Least Squares (GLS) regression technique is employed for the analysis. The study reveals that the financial performance of Tunisian insurance companies is positively influenced by firm size, capital amount, and risk capital management. On the other hand, it is negatively influenced by leverage level, industry size, concentration index, inflation, and unemployment. In terms of technical performance, the capital amount of the firm, industry size, age of the firm, and population growth have a positive impact. However, firm size, leverage, concentration index, and risk capital management negatively affect technical performance. This paper contributes to the existing literature by examining the determinants of performance specifically for insurance companies in Tunisia. Besides the classical proxies of performance, this paper has the originality of using the technical performance which is the most suitable for the case of Insurance companies.

The research is focused on the evolution of the enterprises, in the field of specialized professional services, medium-period, enterprises that implemented projects financed within Regional Operational Program (ROP) during the 2007–2013 financial programming period. The analysis of the economic performance of the micro-enterprises corresponds to general objectives, but there can be outlined connections between these performances and other economic indicators that were not considered or followed through the financing program. The study case is focused on the development of micro-enterprises in the services area, in the Central Region, Romania (one of the eight development regions in Romania). The scientific approach for this article was based on a regressive statistical analysis. The analysis included the economic parameters for the enterprises selected, comparing the economic efficiency of these enterprises, during implementation with the economic efficiency after the implementation of the projects, during medium periods, including the sustainability period. The purpose of the research was to analyse the economic efficiency of the selected micro-enterprises, after finalizing the projects’ implementation. The authors intend to point out the need for a managerial instrument based on the economic efficiency of companies that are benefiting from non-reimbursable funds. This instrument should be taken into consideration in planning regional development at the national level, regarding the conditions and results expected. Although the authors used regressive statistical analysis the purpose was to prove that there is a need for additional managerial instruments when the financial allocations are being designed at the regional level. This study follows the interest of the authors in proving that the efficiency of non-reimbursable funds should be analysed distinctively on the activity sectors.