AI and blockchain drive startup supply chain finance

AI emerged as a crucial enabler in automating credit risk analysis, forecasting cash flows, and streamlining payment cycles. The use of machine learning and data analytics provided predictive capabilities, allowing firms to anticipate financial bottlenecks and respond with precision. According to the results, AI and firm capabilities together explained 57.2% of the variance in SCF outcomes, confirming their strategic relevance.


CO-EDP, VisionRICO-EDP, VisionRI | Updated: 29-05-2025 10:19 IST | Created: 29-05-2025 10:19 IST
AI and blockchain drive startup supply chain finance
Representative Image. Credit: ChatGPT

A new study published in Logistics explores how startups can boost financial efficiency and resilience by leveraging emerging technologies and organizational capabilities. The article, titled “Empowering Startup Supply Chain: Exploring the Integration of SCF, AI, Blockchain, and Trust,” presents a comprehensive empirical model based on 349 Jordanian startups, revealing how artificial intelligence (AI), blockchain, firm resources, and trust interact to optimize supply chain finance (SCF).

The research integrates Resource-Based View and Trust Transfer Theory to frame a technology-enabled SCF ecosystem. Using structural equation modeling, the authors identify how internal capabilities and intelligent digital tools mediate and moderate supply chain finance success, offering a roadmap for digitally ambitious small businesses seeking both liquidity and resilience.

How do AI and firm capabilities directly influence supply chain finance?

The study found that both firm capabilities/resources and artificial intelligence directly enhance SCF performance. Organizational resources, such as skilled personnel, effective communication, auditing abilities, and innovation culture, were shown to significantly contribute to liquidity management, risk mitigation, and working capital optimization.

AI emerged as a crucial enabler in automating credit risk analysis, forecasting cash flows, and streamlining payment cycles. The use of machine learning and data analytics provided predictive capabilities, allowing firms to anticipate financial bottlenecks and respond with precision. According to the results, AI and firm capabilities together explained 57.2% of the variance in SCF outcomes, confirming their strategic relevance.

Notably, the study confirms that AI does not act alone. Its utility is enhanced when combined with strong organizational infrastructure, indicating that even advanced tools need a robust managerial and technical foundation to generate maximum impact.

What role does blockchain technology play in enhancing trust and transparency?

Blockchain was examined both as an independent factor and as a mediating mechanism. The results confirmed that blockchain significantly contributes to enhancing SCF by improving transparency, traceability, data integrity, and transaction security. Firms that adopted blockchain reported streamlined processes such as automated smart contracts, secure documentation, and immutable financial records.

Moreover, blockchain acted as a bridge that transmitted the positive effects of AI and firm resources into tangible financial outcomes. Mediation analysis showed that blockchain accounted for 33.27% of the variance between firm resources and SCF, and 44.31% in the case of AI’s influence on SCF. This indicates that blockchain not only supports but amplifies the impact of internal and digital inputs.

The dual role of blockchain, as both a technological asset and a trust enabler, was critical in reducing information asymmetry and financial risk, especially in ecosystems where traditional bank financing is scarce or slow. For startups in developing economies like Jordan, this presents a compelling case for adopting decentralized ledgers in their financial processes.

How critical is trust in maximizing the value of emerging technologies in SCF?

Trust was tested as a moderating variable across all relationships and emerged as a powerful catalyst in enabling the benefits of firm capabilities, AI, and blockchain. Higher levels of inter-organizational trust led to better collaboration, stronger data-sharing practices, and more streamlined financing decisions.

The study confirmed that trust significantly strengthens the relationships between:

  • Firm capabilities/resources and SCF (β = 0.289),
  • Blockchain technology and SCF (β = 0.335),
  • AI and SCF (β = 0.323).

These findings underscore that technology alone is insufficient. Trust between supply chain actors, especially buyers, suppliers, and financial institutions, is essential to unlocking the full benefits of digital tools. In the absence of trust, even the most advanced platforms may underperform due to stakeholder hesitancy, data hoarding, or delayed decision-making.

The authors note that in supply chains, trust builds reputational capital and reduces transactional friction. Technologies like blockchain inherently promote trust through transparency and verifiability, but cultural and organizational alignment remains necessary for widespread adoption.

Strategic implications and future outlook

The research offers strong theoretical and practical insights. It extends the Resource-Based View by incorporating digital infrastructure and trust as dynamic capabilities in SCF management. The findings also inform practitioners on the design of technology-adoption strategies.

From a business strategy standpoint, firms are encouraged to invest not only in AI tools but also in human capital, communication systems, and trust-building initiatives. Blockchain should be adopted not merely for compliance or security but as a core enabler of finance and operational agility.

For policymakers and ecosystem developers, the study provides evidence to support regulatory frameworks that facilitate blockchain integration and trust-driven supply chain practices in emerging markets.

Despite its contributions, the study recognizes limitations, such as its focus on the Jordanian startup context and its reliance on self-reported survey data. The authors call for cross-country validation, deeper exploration of AI's financial impact, and qualitative case studies to refine the model's generalizability.

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