The increasing complexity of financial systems and strategic environments has intensified the need for advanced decision-making models capable of handling uncertainty, multidimensional risk, and dynamic optimization constraints. Classical management science tools—while robust—often face computational limitations when applied to high-dimensional portfolio optimization, capital allocation, and scenario-based strategic planning. This paper explores quantum-driven financial decision-making models for management science and strategic planning, emphasizing hybrid quantum–classical frameworks that integrate quantum optimization, probabilistic sampling, and advanced analytics into managerial decision systems. By examining applications in portfolio management, risk assessment, supply chain finance, and strategic resource allocation, the study evaluates the theoretical advantages and practical constraints of quantum-enhanced models in organizational contexts. The findings suggest that quantum-driven decision architectures may complement traditional financial models by expanding solution-space exploration and improving optimization efficiency, thereby enhancing strategic agility and long-term value creation.

From text to risk: Predicting repayment risk in supply chain finance with deep learning and large language models

RAFN: A risk-aware feature network for identifying risk factors in supply chain finance

Bank financing or e-commerce platform financing: Green supply chain financing strategies with blockchain integration

How does supply chain finance impact green breakthrough innovation in the supply chain? Knowledge spillover effect based on core firms

Abstract Background With the rapid development of the digital economy and fintech, supply chain finance professionals are often exposed to high-intensity decision-making pressure, uncertainty, and performance-driven work environments, resulting in a significantly higher incidence of mood disorders (such as anxiety, depression, and stress-related disorders) compared to the general workforce. Existing research indicates that mood disorders not only affect individual mental health but also weaken risk assessment and decision-making stability, thereby amplifying the operational risks of the financial system. Traditional psychological interventions often rely on offline cognitive behavioral therapy (CBT), but these methods face challenges such as high participation costs, low compliance, and insufficient intervention timeliness among supply chain finance professionals. In recent years, digital cognitive behavioral therapy (Digital CBT), leveraging mobile platforms, intelligent interaction, and data analysis technologies, has shown promising potential in the early screening and intervention of mood disorders. However, its application in specific high-risk financial professional groups still lacks systematic evaluation. Therefore, this study focuses on supply chain finance professionals, constructing and validating an early intervention model using Digital CBT to assess its comprehensive effects on alleviating mood disorders, improving psychological function, and enhancing intervention compliance. Methods This prospective controlled study included 412 employees of a regional supply chain finance institution. Participants were divided into an intervention group and a control group based on baseline mood scale results. The intervention group received 8 weeks of Digital CBT, including modules on emotion recognition, cognitive restructuring, stress management, and behavioral activation, while the control group received only routine mental health education. All participants completed standardized mood scale assessments at baseline, week 4, and week 8, and intervention usage frequency and completion rates were recorded via a platform log. The study process included participant selection, baseline assessment, implementation of the digital intervention, periodic follow-up, and outcome assessment. Results After 8 weeks, the intervention group showed a 32.6% and 28.4% decrease in anxiety and depression scale scores, respectively, compared to baseline, significantly lower than the 11.2% and 9.5% decreases in the control group (p<.01). Repeated measures ANOVA showed a significant time-group interaction effect (F = 14.37, p<.001). In the medium-to-high risk subgroup, the intervention efficacy rate was 68.9%, higher than the 41.7% in the control group. Platform data analysis revealed that participants with a completion rate exceeding 75% experienced greater emotional improvement (d = 0.53). Furthermore, cognitive flexibility and stress coping abilities significantly improved, showing a moderate correlation with the reduction in emotional symptoms (r = 0.41 ~ 0.47). Discussion This demonstrates that Digital CBT can achieve early, low-cost, and highly adherent intervention for emotional disorders in supply chain finance professionals, demonstrating a clear effect on alleviating negative emotions and improving psychological function. This model provides a scalable pathway for mental health management among high-stress financial professionals. Future research could combine physiological signals and work performance indicators to further assess its long-term impact on risk decision-making and occupational functioning, and explore personalized, adaptive intervention strategies.

Background In the context of accelerating digital transformation, the alignment between digital transformation initiatives and sustainable supply chain finance has become an important issue for firms seeking to improve operational coordination and financial efficiency. This alignment, however, is influenced not only by technological adoption but also by organizational and contextual conditions such as governance structures, strategic orientation, and resource availability. Existing studies have not sufficiently examined how these contextual factors jointly shape the integration of digital transformation and supply chain finance. Methods This study adopts a mixed-methods approach that combines quantitative analysis based on survey data with qualitative insights. A multidimensional analytical framework is developed to examine the contextual factors affecting the integration between digital transformation and sustainable supply chain finance. The framework includes leadership vision, strategic orientation, information-sharing mechanisms, supplier collaboration, organizational resources, process readiness, and access to financial and credit resources. Results The findings indicate that these contextual factors collectively influence the degree of alignment between digital transformation initiatives and sustainable supply chain financial models. Leadership vision and clear strategic orientation play a central role in guiding coordination among supply chain actors. Effective information-sharing mechanisms support collaboration and transparency across partners. In addition, organizational resources and process readiness significantly affect the feasibility and effectiveness of implementing digital solutions linked to supply chain finance. Access to financial and credit resources also conditions firms’ ability to operationalize such initiatives. Conclusion The study provides empirical evidence on how organizational and contextual conditions shape the integration of digital transformation with sustainable supply chain finance. The results suggest that alignment depends on a combination of strategic direction, internal capabilities, and coordination mechanisms, offering useful implications for firms seeking to design digital transformation initiatives that are consistent with their organizational capacities and operational contexts.

Traditional supply chain payment structures create significant liquidity challenges for suppliers through extended payment terms that can span several months, straining business relationships and constraining working capital availability. This article examines how artificial intelligence technologies are fundamentally transforming payment cycles by addressing the core barriers of processing friction and risk uncertainty that have historically necessitated prolonged payment windows. Through detailed article of AI-enabled instant payment frameworks, the article explores three critical components: autonomous invoice liquidation systems that employ neural networks for real-time document validation and approval routing, predictive dynamic discounting engines that optimize early payment offers based on supplier-specific cash flow patterns, and machine learning risk assessment mechanisms that provide instantaneous trust scores for payment authorization. The article investigates implementation frameworks, economic impacts, and organizational challenges associated with transitioning from term-based to data-driven payment approaches. Findings indicate substantial improvements in processing efficiency, working capital optimization, and supplier relationship quality, while also identifying technical, organizational, and regulatory obstacles requiring strategic mitigation. This transformation reframes supply chain payments as data optimization challenges rather than fixed-term obligations, enabling high-velocity digital transaction flows that benefit both buyers and suppliers. The article contributes to understanding how emerging technologies can resolve longstanding tensions in supply chain finance while identifying future directions for technological advancement and scholarly investigation.

Compared with conventional financing approaches, supply chain financing demonstrates superior adaptability in risk management, greater cost-effectiveness in financial control, and enhanced efficiency in approval processes, owing to its deep integration with industrial chains. This investigation explores the intrinsic relationship between digital innovation and corporate supply chain financing. To ensure the rigor and reliability of the research conclusions, we adopt an empirical research method based on the OLS econometric regression model to systematically examine the relationship between digital innovation and supply chain financing. Our findings reveal that digital innovation positively influences corporate operations and information disclosure quality, thereby facilitating supply chain financing acquisition. Specifically, digital innovation enhances both Tobin’s Q and information transparency, which consequently improves firms’ access to supply chain financing. Furthermore, we observe pronounced heterogeneity in digital innovation’s impact on supply chain financing accessibility, with more pronounced effects observed in state-owned enterprises, mature firms, and regions with less developed legal frameworks. From the perspective of theoretical contributions, this study enriches the application scenario of signal transmission theory. We verify that operational improvement driven by digital innovation can serve as an effective signal to alleviate information asymmetry in supply chain financing. Meanwhile, we supplement the research on information asymmetry theory by providing a digital solution to mitigate information frictions between supply chain partners. In terms of practical contributions, we provide actionable insights for firms. Specifically, our findings guide firms to leverage digital innovation to improve supply chain financing accessibility. Additionally, these findings offer references for supply chain stakeholders and relevant authorities to optimize financing support mechanisms.

In the context of rural revitalization and the expanding digital economy, IoT technology offers new opportunities for returning migrant workers, driving agricultural modernization, business innovation, and urban-rural integration. However, challenges such as limited access to capital, technical barriers, market competition, and inadequate infrastructure remain significant obstacles. Using a mixed-methods approach, this study investigates IoT-enabled agricultural ventures across several provinces, revealing that IoT enhances the economic viability of migrant worker-led startups, although outcomes vary by local context. Key limitations include issues with network reliability and financing access. To foster rural entrepreneurship, the study recommends targeted policy support, accelerated 5G infrastructure deployment, and innovations in supply chain finance.

Artificial Intelligence Applications in Intelligent Financial Decision Support Systems: Integrating Big Data and Service Science for Supply Chain Finance Optimization

ABSTRACT Overconfidence is a cognitive bias of believing an uncertain event to be more certain than it is. This bias is prevalent in small retailers whose operations (e.g., product ordering) rely on financing such as bank financing (where banks finance retailers' product ordering) or trade‐credit financing (where retailers order products from their upstream manufacturers without immediate payments). We study which one of the two financing modes should be used to finance an overconfident retailer. Our findings reveal that a higher level of overconfidence harms the retailer under bank financing but does not harm the retailer under trade‐credit financing. Under either financing mode, a higher overconfidence level can benefit the supply chain. We further uncover that the manufacturer should offer trade‐credit financing when the retailer exhibits low levels of overconfidence and should not offer it when the retailer's overconfidence level is high. When the retailer's overconfidence level is high, trade‐credit financing will yield a higher expected profit for the retailer and the supply chain than bank financing. We show that our main findings are robust in a variety of model extensions. Our paper explores the complex interplay between a prevalent psychological factor (overconfidence) in retailers' financing strategies, enriching the literature on supply chain finance and providing insights into the operations of overconfident retailer supply chains.

Deeptier supply chain finance (DTSCF) extends financing beyond firsttier suppliers to small and medium-sized enterprises (SMEs), supporting fullchain environmental, social, and governance (ESG)objectives. This study is the first to integrate blockchain-enabled invoice tokenization into a three-tier game-theoretic model of a green supply chain, jointly capturing credit and liquidity risks under demand uncertainty. We derive closed-form equilibria to quantify how tokenization, with and without factoring, affects operational decisions, green investment, and profits. Our results present three key findings: (1) with moderate liquidity, invoice tokenization significantly enhances supply chain efficiency and increases profits for all parties by resolving credit traps, particularly when combined with factoring; (2) with high liquidity, tokenization stimulates increased production and green investments among tier-2 suppliers and improves supply chain efficiency, though it does not influence tier-2’s factoring decisions; (3) with low liquidity, tokenization offer limited value in encouraging factoring, thereby contributing minimally to supply chain resilience. This research highlights the transformative potential of blockchain-enabled invoice tokenization in promoting sustainable financial practices within supply chains.

Problem definition: Supply chain financing solutions allow financially weaker suppliers to secure funding based on the stronger financial standing of their buyers, presenting an alternative to conventional financing. This paper explores the preferences between immediate-tier financing (involving a direct buyer of the supplier) and remote-tier financing (involving a buyer’s buyer). Methodology/results: Using a model of a three-tier supply chain and a financial market, we solve for the subgame-perfect Nash equilibrium for a game comprising supply chain financing supergame and financing subgames between a firm and investors, subject to equilibrium pricing of firms’ assets-in-place and moral hazard frictions. The game is dynamic and stochastic because production shocks and financing subgames occur over time, coinciding with trade transactions between firms, and because of the stochastic evolution of information about firms’ assets-in-place affecting demand for products of each firm. We identify three economic mechanisms that stem from the relative position of firms in the supply chain and that work against remote-tier financing: risk spillover, noise accumulation, and loss of hedging options. Risk spillover occurs when a firm’s pledge of assets to secure supplier financing increases its own financing risk, affecting upstream firms and transactions. Noise accumulation refers to the greater uncertainty in forecasts of asset values, which are realized later for remote-tier buyers. The loss of the hedging option occurs because, by helping the supplier initially, the remote-tier buyer loses some of the capacity to help the immediate-tier buyer later. These mechanisms may explain challenges faced by remote-tier supply chain financing in practice. Managerial implications: The paper highlights the challenges faced by remote-tier financing and identifies conditions under which it can be favorable. It extends traditional finance models to incorporate supply chain considerations, demonstrating the importance of these factors in economic and financial decision making. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1262 .

ABSTRACT Recently, a quantum blind signature scheme based on the ‐state for quantum supply chain finance has been proposed, which utilizes blindness to protect the privacy of participating firms. However, our analysis found that this scheme cannot resist the intercept‐measurement‐resend attack and the entanglement‐measurement attack, and the blind operation is ineffective against the insider attacker. Importantly, the blind signer can achieve existential forgery through a valid signature. Then, an improved scheme was proposed that reduces quantum bit consumption, eliminates the need for quantum state swap tests, and extends the message space for signatures to the more universal classical bit space. We have constructed a security model that formally defines the security properties and adversary capabilities of the quantum blind signature. Through this model, the blindness and unforgeability of the improved scheme are reduced to the principles of quantum mechanics in four games. The vulnerabilities of the original scheme and the feasibility of the improved scheme in quantum computing have been verified through the Qiskit simulator.

Digital government development, supply chain finance, and the business environment for enterprises

Can corporate data assets gain preference? Evidence from supply chain financing

Navigating the ecosystem innovation: The impact of digital technology adoption in supply chain finance

Supply chain finance, environmental uncertainty and corporate supply chain resilience

Interactions between supply chain financing and information transparency