Purpose This study aims to examine whether firm-level market indicators and lagged macroeconomic information can be integrated within an explainable predictive framework for corporate financial distress, with particular attention to temporal dynamics, model performance and interpretability. Design/methodology/approach This study uses an unbalanced panel of 15,664 firm-month observations from 99 large-cap US listed equity series covering July 2012–September 2025. Regularized logistic regression, Random Forest, HistGradientBoosting and XGBoost are evaluated using chronological training, validation and held-out testing. SHapley Additive exPlanations (SHAP) provide model interpretability, while alternative macroeconomic lag structures, calibration diagnostics and robustness checks assess the stability of the predictive evidence. Findings Regularized logistic regression records the strongest full-test point estimates (AUC = 0.7886; average precision = 0.3085), while XGBoost is the strongest nonlinear benchmark (AUC = 0.7314; average precision = 0.2049). However, model rankings vary across evaluation periods. The combined one-, three- and six-month lag specification performs best, indicating that predictive macroeconomic information is distributed across multiple horizons. SHAP identifies 63-day firm-level volatility as the leading XGBoost feature and reveals conditional relationships between macroeconomic conditions and firm-specific vulnerability. Research limitations/implications The large-cap US sample, market-based distress definition and mixed-frequency design limit broader generalization. Practical implications The framework can support risk ranking, early-warning screening and interpretable financial-risk monitoring for investors, financial institutions and regulators. Originality/value This study integrates temporal macroeconomic information, comparative model evaluation and explainable AI, demonstrating that greater model complexity does not necessarily improve out-of-sample distress prediction.
Purpose This paper systematically reviews the use of machine learning (ML) and data-driven models in financial risk and liquidity management within large-scale projects, targeting the multifaceted challenges arising from economic, contractual and contextual complexities. This study aims to explore how data-centric approaches can strengthen forecasting, control cash flow volatility and improve resilience across infrastructure, energy and public-sector projects. Design/methodology/approach Following the PRISMA protocol, 142 peer reviewed studies published between 2012 and 2025 were comprehensively screened and analyzed. The sample includes 40 % quantitative, 32 % mixed methods, 16 % literature review and 11 % qualitative designs, demonstrating a balanced methodological landscape. The studies were synthesized across three interrelated domains, i.e. (i) data, (ii) model and (iii) application to capture technical, analytical and contextual dimensions of financial risk management. Findings The reviewed literature reveals clear research concentration patterns, with ML-based models, particularly ensemble methods, neural networks and decision tree algorithms, emerging as the dominant analytical tools, especially in credit risk assessment and related financial decision contexts. Credit risk-related research (30%) dominates the literature, while project-level liquidity remains underexplored (≈ 4%), marking a significant conceptual gap. Persistent limitations include lack of accurate historical data (28%), model transparency issues (19%) and market-specific bias (18%). These constraints highlight the need to improve data quality, enable interpretability and adapt models across heterogeneous project contexts and economic systems. Research limitations/implications This review is limited by potential selection bias due to language restrictions and the exclusion of unpublished or gray literature. The heterogeneity in methodological quality and reporting among included studies complicates direct comparison and synthesis of findings. The reliance on secondary data and simulation models also underscores the need for more empirical, context-specific studies. Future research should aim to address these gaps, explore broader regional contexts and validate ML models under real-world project conditions. Practical implications The findings highlight the superior predictive power of ML models in managing financial risks and liquidity in large-scale projects, facilitating more informed and proactive decision-making. Practitioners can use hybrid and interpretable models to optimize risk assessment processes and adapt financial strategies to dynamic project environments. However, effective implementation requires addressing data quality issues, ensuring model adaptability to local conditions and enhancing stakeholder collaboration across project teams. Integrating advanced analytics into existing project finance frameworks can drive more resilient, data-driven risk management and improve overall project outcomes. Social implications Adopting advanced ML and data-driven risk management methods can enhance transparency and accountability in project finance, supporting socioeconomic development and stability. Improved risk mitigation reduces the likelihood of project failures, cost overruns and financial crises that impact broader communities. Better liquidity management enables timely delivery of essential infrastructure, public services and employment, contributing to sustainable urban growth and improved living standards. Bridging the digital divide and addressing ethical considerations in AI usage will ensure equitable benefits for diverse stakeholders and promote public trust in technological advancements. Originality/value By integrating findings within the three-domain (data–model–application) framework, this review offers a structured synthesis of ML’s impact on financial risk and liquidity management. It delineates empirical performance, contextual adaptability and methodological challenges providing actionable directions for bridging theoretical design and real-world decision systems. The study delivers a clear roadmap for researchers and policymakers to strengthen transparency, data governance and cross-disciplinary collaboration in project finance analytics. Rather than promoting specific algorithms, this review maps the structure, focus and blind spots of existing research, providing a consolidated evidence base for future methodological and application-oriented studies.
Purpose The rise in environmental degradation demands a shift from linear to circular economy practices. The study aims to develop a circular economy model that integrates it with third-party recovery service providers (3PRSPs) to tackle real-world capacity constraints to improve the performance of the circular supply chain. Design/methodology/approach A multi-objective circular economy model is formulated based on (1) product-mix profitability maximization, (2) environmental deterioration minimization, and (3) non-recoverable product reduction. Two scenario analyses are conducted to analyze the impact of a centralized and decentralized circular economy network. The model is solved using neutrosophic optimization to account for uncertain product return rate and demand. The methodology is also compared with the epsilon-constraint method and interactive multi-objective fuzzy programming, highlighting the superior ability of neutrosophic optimization to handle higher-order uncertainty. Sensitivity analysis is performed by changing the selling price of new and recovered products to assess the financial viability of the model. Findings Scenario B with a centralized circular economy network yields higher profitability due to greater reliance on third-party recovery and reduced fixed costs. Scenario A, featuring a decentralized circular economy network, reduces environmental impact due to localized recovery and shorter logistical distances. Non-recoverable products remain the same in both network designs, underscoring the inefficiency in raw material quality and product design. Sensitivity analysis also indicates that profitability is highly sensitive to the new product pricing strategy. In addition, neutrosophic optimization enhances the robustness of circular economy network design under uncertain conditions. Originality/value The study provides a robust decision-making framework for the integration of the circular economy network with 3PRSPs under capacity constraints. By utilizing a neutrophic approach, this model accounts for demand and return rate uncertainty and fills the gap between theoretical frameworks and practical implementation of the circular economy system. The findings provide insights for researchers, managers and policymakers that strategic network design and collaboration with third party for recovery operations can improve economic, environmental and operational dimensions of the circular economy.
Purpose This study aims to examine how Fourth Industrial Revolution (4IR) technologies can transform telecom asset maintenance management in developing countries, with a focus on Nigeria’s telecom operations. As a developing country with rapidly growing telecom operations, Nigeria faces unique opportunities and challenges in adopting intelligent technologies such as blockchain, digital twins (DTs) and artificial intelligence to improve infrastructure and asset uptime and reliability, reduce operating and maintenance costs and enhance network functionality, thereby improving network availability and service quality. By examining the effectiveness of emerging 4IR technologies in Nigeria’s distinct infrastructure and asset domain, this study offers valuable insights for practitioners seeking to harness these technologies to enhance reliable, efficient telecom asset maintenance management. Design/methodology/approach Using a mixed-methods approach that includes quantitative and qualitative research techniques, supported by extant literature on asset management, we explore how emerging 4IR technologies can address the unique challenges faced by telecom managed services in Nigeria, particularly in terms of infrastructure and asset maintenance, operational efficiency, network availability and service quality. The research design, population sampling methods, data collection approaches and analysis frameworks guided this study on how developing countries can leverage 4IR technologies to enhance telecom infrastructure and asset maintenance management. Findings The findings were established based on the validity of the examined hypotheses, as evidenced by the visibility of asset functionality, monitoring and performance resulting from the application of 4IR technologies, such as DT. These technologies significantly enhance operational reliability and availability by reducing mean time to repair (MTTR). Furthermore, the findings have implications for operational efficiency, cost management and the broader digital revolutionisation agenda in developing countries. DT technologies facilitate a predictive maintenance approach by proactively escalating unforeseen asset outages, reducing MTTR and operating costs. Originality/value The study complements the existing literature by evaluating the 4IR technologies required to manage telecom assets in developing countries, given infrastructure deficiencies.
Purpose The purpose of this study is to investigate the integration of additive manufacturing (AM) with traditional manufacturing (TM) in supply chains (SCNs), aiming to meet diverse customer needs, optimize production time and cost and enhance competitiveness in uncertain market environments. Design/methodology/approach The authors develop a game-theoretic SCN model under uncertainty, focusing on two substitute products – one produced through TM and the other through AM. Customer demand depends on product quality and sales effort. They analyze centralized and decentralized decision-making across three scenarios: (1) production through TM only, (2) production through AM only, and (3) production through both TM and AM. The model is validated through numerical examples, and sensitivity analysis is conducted to examine the effects of key parameters on SCN profit. Findings Results show that integrating AM with TM enhances operational efficiency and overall SCN profitability. While AM reduces unit production cost compared to TM, its installation cost may reduce profitability if it exceeds a critical threshold. Improved product quality and increased sales effort significantly boost profits, and accounting for demand uncertainty enables better alignment with customer preferences, leading to mutual gains for SCN members. Originality/value This research is among the first to examine SCN decision-making by combining AM and TM under demand uncertainty using a game-theoretic framework. It offers theoretical insights into the conditions under which AM adoption is profitable and provides practical guidance for managers seeking to balance investment in AM with traditional practices for improved competitiveness and sustainability.
