Empowering Startup Supply Chain: Exploring the Integration of SCF, AI, Blockchain, and Trust

Abstract

Background: This research aims to comprehensively evaluate the influence of firm capabilities, resources, and Artificial Intelligence (AI) on supply chain financing within the Jordanian context. It also analyzes the mediating role of blockchain technology and trust in these relationships. Methods: A conceptual model was utilized to empirically examine the suggested relationships. Data were gathered from a sample of 349 Jordanian start-ups focusing on AI and blockchain technologies via a five-point Likert scale questionnaire. Partial Least Square regression-based Structural Equation Modeling (PLS-SEM) facilitated by SmartPLS4 was used to perform the analysis. Results: The findings reveal that firm capabilities/resources and AI positively impact supply chain financing. Blockchain technology and trust serve as mediators, enhancing the effects of these factors on supply chain financing. Conclusions: The study highlights the role of innovative technologies in improving financial information security and collaboration among supply chain partners. It emphasizes how firm capabilities, resources, and emerging technologies such as AI and blockchain influence supply chain financing as they foster trust and security in financial transactions, offering valuable insights for decision-makers in the supply chain domain.

1. Introduction

Digitalization and disruptive technologies are changing innovative international business tendencies from conventional businesses to digitally transformed businesses. Such technologies are vastly influencing Supply Chain Finance (SCF) and its global application. SCF is an emerging research field and an important source of competitive advantage. SCF provides a complete solution for financial management matters in the supply chain as it assists buyers and suppliers in managing their working capital. Suppliers also benefit from using trade credits as it permits them to schedule the payments on their favorable dates without disturbing the firm’s cash flow [1].

Additionally, buyers who utilize SCF may obtain numerous advantages, such as extending the due dates for payment, as it allows faster cash access to suppliers to manage the working capital. SCF is important in expanding vendors’ liquid assets and working capital to increase business effectiveness, where buyers benefit from delayed payouts [1]. Therefore, SCF is believed to offer efficient cash flow management solutions to maintain the relationship between different suppliers and financial institutions in the supply chain. For instance, SCF works as a “factoring reversal”, allowing buyers to make early payments of their trade credits to the suppliers. However, the buyer or supplier must assess their relationship with financial institutions, whether they are high or low-risk customers [2,3].

Traditional supply chain financing models, such as factoring, reverse factoring, and trade credit, have long served as essential mechanisms for enhancing liquidity and optimizing working capital across supply chains. These conventional practices rely heavily on manual processes, paper-based documentation, and third-party financial institutions that assess creditworthiness through historical financial data and static risk models [4]. While effective to a degree, these models often suffer from issues of limited transparency, time-consuming approval processes, and asymmetric information that can disadvantage smaller suppliers with weaker bargaining power.

In contrast, AI-blockchain-based SCF models leverage distributed ledger technologies and intelligent analytics that enhance visibility, traceability, and trust across supply chain stakeholders. Artificial Intelligence (AI) enables real-time credit evaluation based on alternative data sources, predictive modeling, and risk profiling, allowing more inclusive financing decisions. Moreover, blockchain guarantees data immutability, auditability, and shared access to transactions, therefore reducing the necessity for intermediaries and potential fraud [5].

Despite the growing body of research exploring the impact of firm capabilities, resources, AI, and blockchain technology on supply chain financing, the role of trust remains unexplored in the Jordanian context. Previous studies have focused primarily on the individual effects of these elements rather than their combined impact on supply chain financing, with blockchain technology and trust serving as moderators, leaving room for further investigation. By addressing this research gap, the study aims to expand the understanding of how firm capabilities, resources, and AI influence supply chain finance decision-making and explore these relationships, thereby contributing to the existing literature and offering valuable insights for practitioners seeking to optimize their supply chain financing operations. Considering the gaps, this study aims to answer the following research question:

• RQ1 “How do firm capabilities/resources and artificial intelligence affect supply chain finance through the mediation of blockchain technology and trust as moderator?”
• While this study provides valuable insights into supply chain dynamics within the Jordanian context, it is important to acknowledge that the findings are shaped by the country’s specific socio-economic conditions. Jordan’s limited resources, import dependence, and distinctive regulatory and labor market structures present characteristics that differ significantly from those of larger or more industrialized economies. These factors may influence the applicability of some conclusions to other regions, particularly in global supply chains that span more diverse economic environments. Therefore, the study’s scope is context-specific, and readers should interpret the results with this in mind.

2. Literature Review

Supply chain finance refers to the use of financial instruments, practices, and technologies to optimize the management of working capital and liquidity in supply chain processes for collaborating business partners [6]. It aims to integrate the flows of information, logistics, and funds within the supply chain, transforming the risks of individual enterprises into controllable risks for the entire chain [7]. This finance service mode focuses on the trading process rather than bank credit, providing comprehensive finance services with minimal risk, especially for companies that face difficulties in accessing traditional finance institutions [8,9]. Supply chain finance can help eliminate obstacles of information asymmetry between banks and enterprises, can improve the core competitiveness of commercial banks, and is able to bring new profit growth for both banks and third-party organization enterprises [10]. SCF expands the traditional scope of supply chain management by incorporating the flow of financial resources, aiming for efficient capital flow and availability where needed [11].

SCF faces challenges such as information imbalances, restricted capital flow, and operational inefficiencies that hinder flexibility and competitiveness within supply chains. As the number of participants in the supply chain increases, a trust gap emerges among enterprises, especially between core enterprises and lower-tier suppliers, leading to a breakdown of trust. Overcoming these challenges necessitates a strategic approach, effective communication, technical innovation, and collaboration among all stakeholders involved in supply chain finance. Furthermore, effective communication is essential for nurturing trust in supply chain financing through fostering open channels of communication and sharing information in real-time through AI-powered platforms, stakeholders can build stronger relationships based on transparency and accountability [12].

Blockchain is a cutting-edge, decentralized, and distributive technology that guarantees all transactions and data privacy, availability, and integrity [13]. It may bring improvement to the existing practices of firms, such as enhancing the security, trust, and transparency of various business procedures [14]; AI and blockchain technologies are both complementary to each other by their design and to unlock their true potential, these two must be integrated [15].

In supply-chain finance, blockchain builds trust between the concerned partners responsible for managing the rolling stocks. Regardless of the known importance of blockchain technology from a financial perspective, it still requires novel contributions to advance the field. Blockchain technology comprises of few key attributes: it is secure, indelible, coordinated, distributed, and sustainable; consensus-based, transactional, and transparent, among other attributes [16], as blockchain technology works with a distributed set of databases that are controlled by persons who manage the relevant information. Blockchain technology is divided into three forms: private, public, and consortium blockchains; these categorizations are based on several factors like transaction, speed, ownership, security, and access rights [17]. Therefore, existing studies [16,18] assert that blockchain technology instigates trust in financial transactions and allows one to make decentralized decisions.

The study of AI focuses on developing or programming computers to mimic human intelligence [19]. When applying AI, it connects SCF networks and fosters digitalization in various supply chain practices such as digital material management, technology-enabled cash flows, and automatic networking among stakeholders to meet customer expectations [20]. Incorporating innovations in the supply chain management domain provides the basis for AI implementation and the advantages of advanced data analytic instruments comprising intelligent networking systems [21]. AI has been available for decades, although not fully utilized, particularly in the supply chain management context worldwide [22]. Therefore, AI has large potential in the financial sector as it may provide enhanced customer experience, reduce operating and business costs, and allow firms to exploit new markets.

Moreover, it is vital to integrate evolving technology such as green cloud multimedia networking and AI, as these provide reflective implications for enhancing organization, trust, and capabilities through blockchain integration [23]. Green cloud multimedia encourages energy-efficient resource allocation by eliminating hardware dependencies and enabling dynamic infrastructures, which is essential for an organization to maintain sustainability [24]. AI enables ideal traffic management and real-time decision-making, which are significant for resource optimization and planning coordination in complex supply networks [25]. These innovations collectively strengthen firm-level capabilities and inter-organizational trust, unleashing the transformative potential of AI and blockchain in supply chain financing.

Emerging technologies are reshaping the context of SCF and logistics. The Internet of Things (IoT) assists real-time data exchange and system visibility across stakeholders, allowing automation of inventory management, order tracking, and financial settlements. These capabilities permit firms to make informed, data-driven financial decisions, improving liquidity and mitigating transaction risk [26].

Building on the IoT, the Software-Defined Internet of Things (SD-IoT) introduces a network architecture where control logic is decoupled from data transmission, allowing for agile reconfiguration and centralized orchestration. This flexibility is important in dynamic supply chains where financial conditions or logistical demands change rapidly. In tandem, QoS-aware SD-IoT frameworks are being developed to ensure performance metrics such as latency, throughput, and reliability guaranteeing that time-sensitive financial data are transmitted and processed with minimal disruption [27].

Moreover, the Software-Defined Internet of Multimedia Things (SD-IMT) extends these capabilities to include real-time multimedia communication within supply chains. Multimedia monitoring tools such as warehouse surveillance, cargo condition monitoring, or vehicle dashcams contribute to enhanced transparency, security, and compliance assurance, all of which are critical aspects in SCF environments where creditworthiness and operational visibility are intertwined [28]. Additionally, smart transportation systems are also evolving through the Software-Defined Internet of Vehicles (SD-IoV), which enables Vehicle-to-Network (V2N) streaming for location tracking, environmental sensing, and vehicle diagnostics. Streaming capabilities facilitate live decision-making for fleet rerouting, load balancing, and just-in-time delivery, thus influencing financing decisions tied to delivery timelines and cargo integrity [29].

Furthermore, Fog-IoT systems are supporting data processing, reducing response times and bandwidth demands. By integrating Artificial Optimization (AO) techniques and the Whale Optimization Algorithm (WOA), Fog-IoT systems support dynamic scheduling, load balancing, and resource allocation. These optimizations are valuable for decentralized financial tasks such as localized credit scoring or vendor performance evaluation [30].

At the same time, it is necessary to take into consideration the potential biases in AI models and data privacy concerns arising from large datasets. Although AI systems are capable of detecting biases if trained on unreliable or skewed data, it may lead to unfair decision-making processes in SCF applications [31]. In addition, data privacy is critical, particularly in financial transactions, where sensitive information must be protected in compliance with regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) [32]. Therefore, employing approaches such as bias detection, correction techniques, and robust security measures, including encryption and access controls is crucial to mitigate these risks and maintain trust across the blockchain-enabled SCF ecosystem [33].

Moreover, AI and blockchain may also encounter critical regulatory and legal challenges. The nature of Blockchain disputes regulations such as the EU’s GDPR, which mandates data elimination that is incompatible with blockchain’s immutability [34]. Additionally, compliance with Anti-Money Laundering (AML) and Know-Your-Customer (KYC) regulations remains challenging on decentralized platforms, especially in cross-border SCF operations [35]. Addressing these legal complexities is essential for scaling blockchain-based SCF solutions effectively and securely.

Trust consists of cognitive processes and communication patterns among supply-chain members. It is the dyadic relationship between trustee and trustor, where the trustor evaluates whether to trust or not. Trust affects the nature of relationships, such as privacy levels, security concerns, and purchase intentions [36]. Also, the adoption of disruptive technologies like blockchain in the supply chain may derive trust from the image and reputation of their intermediary network [37].

Trust plays an essential role in supply chain financing; it serves as a foundational element that underpins successful collaborations and transactions among various stakeholders. According to some scholars [38], trust is essential for fostering cooperation, reducing transaction costs, and mitigating risks in supply chain financing. Establishing trust is crucial for enabling efficient financial flows and ensuring the smooth functioning of supply chains. Therefore, building and maintaining trust in the context of AI and blockchain technology involves several key strategies. Firstly, transparency plays a vital role in enhancing trust within supply chain financing processes. By leveraging blockchain technology, stakeholders can access real-time, immutable records of transactions, thereby increasing transparency and reducing the likelihood of fraud or errors [39]. Additionally, the use of AI algorithms can help analyze vast amounts of data to identify patterns and anomalies, further bolstering trust by enhancing decision-making processes based on accurate insights [40]. These technological advancements not only streamline operations but also contribute to building trust among participants in the supply chain ecosystem.

Furthermore, continuous communication and collaboration are essential for nurturing trust in supply chain financing. By fostering open channels of communication and sharing information in real time through AI-powered platforms, stakeholders can build stronger relationships based on transparency and accountability. Maintaining trust requires a proactive approach to addressing issues promptly and effectively, thereby demonstrating reliability and commitment to ethical business practices [41]. By integrating firm capabilities, resources, AI, blockchain technology, and trust into supply chain financing practices, organizations can unlock new opportunities for growth, innovation, and sustainable partnerships in an increasingly interconnected global marketplace.

Building and maintaining trust among stakeholders in supply chain finance faces several common challenges. One challenge is the lack of shared trust, which can hinder the implementation of precise data and smart business operations [42]. This lack of trust can lead to difficulties in data sharing and impact management choices [43]. Another challenge is the need for transparency in the flow of goods, which requires accurate and timely information accessible to all stakeholders [44]. Additionally, the use of traditional consensus algorithms in blockchain technology has limitations on the nodes’ ability to agree, posing a challenge to trust. Trust is both a challenge and a prerequisite, as it is necessary to build confidence and enable seamless and secure data sharing. Therefore, decision-makers need to employ various strategies for enhancing trust in supply chain finance including the use of blockchain technology [12]. Blockchain-based solutions provide a secure and transparent platform for sharing information and conducting transactions, which helps to build trust among supply chain partners [45]. Moreover, the implementation of smart contracts can help solidify payment paths and control risks, further enhancing trust in the supply chain [41].

3. Theoretical Background and Hypotheses Development

This study utilized the Resource-Based View (RBV) theory to describe internal organization assets e.g., capabilities, resources, and emerging technologies such as AI that facilitate the performance in Supply Chain Finance (SCF). According to RBV, organizations gain a competitive advantage by acquiring Valuable, Rare, Inimitable, and Non-substitutable (VRIN) resources [46]. In SCF, AI technologies are considered a vital asset that supports decision-making, process automation, and risk mitigation. Additionally, blockchain technology contributes to enhanced transparency, security, and traceability in financial transactions. Thus, RBV provides a robust theoretical foundation for understanding how firms influence the internal and technological resources to drive SCF efficiency.

This research draws on Trust Transfer Theory to explain the role of trust in the relationship between blockchain technology and SCF outcomes. In supply chains, blockchain is often perceived as a robust technology that influences trust effectively [47]. Consequently, stakeholders are more likely to accept blockchain for SCF solutions if trust is high. Therefore, Trust Transfer Theory is suitable to explain the determinant influence of technological interventions in SCF.

3.1. Firm Capabilities and Resources and Supply-Chain Finance

According to the Resource-Based View (RBV), the company’s current capabilities are the foundation for furthering and enhancing its skills through ongoing, collaborative learning [48]. Research provides little information as to what types of capabilities are the most important for collaboration under what contexts, bearing in mind that they are critical success factors for collaboration. Firms’ IT capabilities can make business information more visible in the supply chain network, allowing network members to make integrated decisions [49]. Rapid access to the firm’s business information enhances the trust and fair distribution of supply chain resources as it lowers the credit risks and speculative behaviors within the supply chain [3]. Furthermore, the efficient utilization of firm resources, such as human and financial resources, along with strategic analysis capability, has been found to have a positive and significant relationship with strategic performance [50]. Firm capabilities and resources play a crucial role in supply chain finance and can contribute to improved performance in both supply chain and overall firm operations.

Firm capabilities and resources are considered foundational to the effective implementation and optimization of Supply Chain Finance (SCF) practices. Technological capabilities, including infrastructure flexibility and integration, are critical enablers of SCF as they enhance supply chain agility and absorptive capacity [51]. These capabilities allow firms to process and integrate financial and operational data in real time, facilitating more accurate credit assessments and faster decision-making. Furthermore, high IT capability levels reduce financial risk exposure for SCF service providers, making financially stronger and technologically equipped firms more attractive participants in SCF arrangements [52]. Similarly, operational capabilities, such as process improvement and innovation, allow firms to manage supply risks more effectively, thus strengthening their financial standing and reliability in SCF ecosystems [53]. Lean operational practices, built on resource complementarity and specificity, enhance both operational and financial outcomes, emphasizing the importance of well-developed supply chain resources in realizing SCF benefits [54].

Moreover, dynamic capabilities, human capital, and stakeholder engagement are also vital. Dynamic capabilities equip firms to adapt swiftly to unstable environments such as demand shifts or resource constraints thus maintaining continuity and reliability in SCF participation [55]. Human capital and stakeholder support further enhance supply chain integration, coordination, and trust key enablers for collaborative financial arrangements. Investments in supply chain-specific assets, strengthened by digital technologies, deepen inter-firm relationships and improve a firm’s ability to design and implement tailored SCF solutions [56]. The following table (

Table 1. Impact of Capabilities/Resources on SCF.

Category	Key Capabilities/Resources	Influence on Supply-Chain Finance (SCF)
IT Capabilities	IT infrastructure flexibility, IT assimilation	Enhance agility, absorptive capacity, and risk mitigation in SCF systems.
Operational Capabilities	Process improvement, innovation, lean practices	Improve supply risk management and financial performance, supporting stronger SCF outcomes.
Dynamic Capabilities	Adaptability, responsiveness to change	Enable firms to manage uncertainties and maintain SCF engagement during market fluctuations.
Human Capital and Stakeholder Support	Skilled personnel, supportive partnerships	Facilitate integration and coordination needed for effective SCF implementation.

) shows the relationship between firm capabilities and SCF.

Therefore, this leads to the first hypothesis:

H1. 

Firm capabilities and resources positively influence Supply Chain Finance.

3.2. Artificial Intelligence and Supply Chain Finance

AI has numerous applications in supply chain finance, including risk management, fraud detection, working capital management, and supply chain optimization [57,58,59]. By analyzing data from various sources, AI can identify potential risks, suggest strategies for managing them, and help prevent fraud [60]. AI can also optimize working capital management by analyzing inventory levels, order volumes, and payment terms. Additionally, AI can improve supply chain efficiency by analyzing order volumes, shipping times, and other factors. The integration of AI in supply chain finance can lead to improved financial performance, reduced costs, and enhanced competitiveness. AI algorithms need to be trained with accurate and reliable data, and they should be able to handle the increasing complexity of the supply chain. Overall, AI has a positive impact on supply chain finance by optimizing processes, improving decision-making, and reducing costs. Consequently, the following hypothesis is proposing as follow:

H2. 

Artificial Intelligence positively influences Supply Chain Finance.

3.3. Firm Capabilities/Resources and Blockchain Technology

The managerial and technological capabilities of a firm are considered firm characteristics that are important for converting inputs into outputs. These characteristics enable repetitive usage of best practices to increase a firm’s value and operational efficiency by utilizing resources throughout the value chain [61]. A firm’s capabilities and resources may allow blockchain technology to lower transaction costs, foster trust among partners, authenticate property, and modify settlement times while providing digital documentation [62]. Firms’ capabilities and routines may foster trust within the supply chain networks and allow blockchain technology to be successfully implemented. Based on the trust transfer theory, customers’ long-term trust in digital payment services may impact how much they trust the firms’ capabilities compared to traditional offline methods [63]. Therefore, firm capabilities/resources are indeed positively related to blockchain technology.

The integration of Artificial Intelligence (AI) into blockchain technology introduces a range of transformative benefits. AI enhances smart contract security, enables real-time threat detection, and strengthens system integrity. Through applying intelligent monitoring techniques and predictive models, AI helps detect anomalies and respond to threats in real time, addressing one of blockchain’s most critical vulnerabilities. This dynamic approach significantly improves the trust and resilience of decentralized systems [64,65]. AI also contributes to improved efficiency and autonomy within blockchain networks [66]. Optimization algorithms powered by AI enhance operational effectiveness by improving resource management and reducing latency in consensus mechanisms. In addition, it increases the autonomy and credibility of decisions made by decentralized systems [67,68]. Through machine learning and data-driven models, AI enables automated decision-making in smart contracts and decentralized applications, reducing human dependency and enhancing system reliability. This directly contributes to the credibility and scalability of blockchain implementations [69,70].

Furthermore, the interaction between AI and blockchain has increased the technology’s practical applications across diverse sectors such as finance, healthcare, supply chain management, and e-voting [71]. AI’s data analysis capabilities complement blockchain’s transparency and immutability, resulting in more intelligent and secure systems [72]. Despite challenges like scalability, implementation costs, and regulatory issues, AI also offers viable solutions such as predictive analytics for resource optimization and improved compliance strategies [73,74]. The table below (

Table 2. Impact of AI on Blockchain.

Aspects	Impact of AI on Blockchain
Security	Enhances smart contract security, real-time threat detection, and system integrity
Efficiency	Optimization algorithms, improved operational effectiveness
Autonomy and Credibility	Increases autonomy and credibility of AI decisions
Applications	Finance, healthcare, supply chain, e-voting
Challenges	Scalability, implementation costs, regulatory issues

) clarifies that AI not only supports but actively enhances the functionality and adoption of Blockchain technology.

As a result, the following Hypothesis is proposed:

H3. 

Firm capabilities/resources positively influence blockchain technology.

3.4. Artificial Intelligence (AI) and Blockchain Technology

Blockchain technology can automate cryptocurrency payments and provide secure and decentralized access to shared data and transactions [75]. Meanwhile, AI offers intelligence and decision-making capabilities similar to humans [76]. By combining blockchain and AI, it is possible to create more efficient and trustworthy business processes, such as using blockchain to track and secure smart contracts and using AI to automate their execution. The integration of blockchain and AI also optimizes the quality of intelligent services and enables secure storage and analysis of data [77]. Researchers have studied the combination of these two technologies and have found that their combination can significantly advance the underlying architecture of the blockchain and improve AI. Integrating blockchain and AI results in increased security, efficiency, and productivity in various application areas.

Blockchain technology adoption within firms is genuinely rooted in the internal capabilities and resources of the organization. Firms with strong intangible capital and dynamic capabilities are well-positioned to initiate proactive and innovative strategies [78]. These capabilities allow firms to steer uncertainty and quickly adjust to technological disruptions such as blockchain, consequently enabling its integration into business processes. Technological capabilities along with a culture of innovation and effective information sharing play a vital role in driving blockchain adoption. Firms that own advanced technological infrastructure and encourage knowledge exchange are more likely to implement blockchain solutions [79]. These capabilities reduce resistance to change and prompt an attitude of openness and learning that are necessary for adopting decentralized technologies. Furthermore, entrepreneurial orientation supports the relationship between internal capabilities and blockchain adoption. Firms with high entrepreneurial orientation are often more willing to take calculated risks, embrace innovation, and pursue new market opportunities traits that align well with the blockchain technology environment [80].

In addition, the accumulation of technological and capital resources enhances operational efficiency and supports continuous innovation [81]. These resources permit firms to handle the costs and complexity associated with implementing blockchain technology while attaining value from its application. Finally, firms aiming to strengthen their competitive performance may be particularly motivated to adopt blockchain, as it offers traceability, transparency, and immutability key attributes for building trust and differentiation in competitive markets [82]. The table below (

Table 3. Interaction Between Firm Capabilities/Resources and Blockchain Adoption.

Capability/Resource	Impact on Blockchain Adoption	Resulting Benefits
Intangible Capital and Dynamic Capabilities	Facilitates proactive and innovative initiatives	Improved firm performance in uncertain environments
Innovation, Information-sharing, Technological Capabilities	Facilitates blockchain adoption	Enhanced sustainability and competitiveness
Entrepreneurial Orientation	Mediates the relationship between orientation and performance	Improved firm performance
Technological and Capital Accumulation	Enables better operational efficiency and innovation	More pronounced benefits from blockchain adoption
Competitive Performance	Offers traceability, transparency, and immutability	Enhanced competitive performance

) summarizes the interaction between specific firm capabilities/resources, their influence on blockchain adoption, and the resulting benefits:

Based on the above, the following hypothesis is proposed:

H4. 

Artificial intelligence positively influences blockchain technology.

3.5. Blockchain Technologies and Supply Chain Finance

Blockchain technology and its applications contribute significantly to supply chain finance by fulfilling the basic functions, e.g., authentication, reliability, and traceability of data [83,84]. By introducing blockchain technology in supply chain finance, it is possible to overcome risk points and information asymmetry problems that exist in traditional supply chain finance [85]. Blockchain-enabled Supply Chain Finance (BSCF) is a key aspect of enhancing financing performance by solving information asymmetry problems and facilitating a transformation to new business models [86]. Moreover, it has been found to have a positive impact on supply chain finance. It helps address challenges related to transparency, traceability, and trust in supply chain finance [87]. Therefore, this research suggests the following hypothesis:

H5. 

Blockchain technology positively influences supply chain finance.

3.6. Blockchain Technology as a Mediator: Firm Capabilities/Resources and Supply Chain Finance

Blockchain provides a platform in which every node contains a duplicate dataset that helps the supply chain members make direct transactions [83]. Blockchain technology serves as a mediator in the relationship between firm capabilities/resources and supply-chain finance [1]. It helps improve integration, agility, and security in the supply chain by enabling real-time information sharing, end-to-end visibility, transparency, and data management [1]. Blockchain technology has notable advantages such as smart contracts and product traceability, which can resolve supply chain issues and enhance supply chain capabilities and flexibility [20]. By introducing blockchain technology in supply chain finance, it addresses risk points and information asymmetry problems, leading to stable development and optimization of the process [2]. Inter-firm characteristics, such as trading partner trust, initial firm power, dependency between partners, knowledge sharing, and cooperation, all are believed to influence the acceptance of blockchain technology in supply chains [4]. Thus, this research proposes the sixth hypothesis as follows:

H6. 

Blockchain technology serves as a mediator in the relationship between firm capabilities/resources and supply chain finance.

3.7. Blockchain Technology as a Mediator: AI and Supply Chain Finance

AI is the capability of the technology-enabled system to perform complex human intelligence-based tasks and has the potential to exceed human capacities [88]. AI is a key driver in bringing industrial development because of its contributions to incorporating innovative technologies in the fourth industrial revolution [88]; examples include cryptocurrency [89], the Internet of Things (IoT) [90], cloud computing [91], and blockchain technology [92].

To meet the future challenges of next-generation industrial development, IR4.0, it is necessary to integrate artificial intelligence and blockchain technology. The authors of [72] argued that both technologies have a two-way effect and that blockchain technology extends the privacy, explainability, and trustworthiness of AI-based applications, while AI enhances the security and scalability of blockchain technology. Although AI and blockchain technology have many technical differences, both may overcome each other’s shortcomings. Some researchers explained that AI supports the firm to identify, understand, and decide during blockchain technology as it executes, verifies, and records business information [72]. Incorporating these two technologies reinvents the financial sector by improving transaction speed and fostering trust among the concerned members. In addition, when utilized in the conventional supply chain, blockchain technology and its integration with smart contracts, IoT, and AI may not only overcome obstacles but also bring in new revenue streams and enhance the business model financially and operationally [93]. Therefore, this study assumes that:

H7. 

Blockchain technology serves as a mediator in the relationship between artificial intelligence and supply chain finance.

3.8. Trust as Moderator

There is much literature that discusses trust in the supply chain context. While supply chain finance is new, it is discussed as an important management element. Trust is viewed as an important predictor of technological reliance, and the degree of interaction between trust and technology is identified as “calibration”, and both potentially impact technology usage and its outcomes. A higher level of trust in disruptive technology may cause over-trust that leads to the misuse of technological resources, which subsequently may result in trust and safety breaches that can potentially end in other detrimental outcomes [36].

Trust in supply chain networks plays a moderating role in the relationship between firm capabilities/resources and supply chain finance [94]. Previous research has considered the positive effects of trust on supply network resilience and firm performance [9,95,96]. Therefore, based on the above the following hypothesis is constructed as follow:

H8 (a).

Trust in supply chain networks moderates the relationship between firm capabilities/resources and supply chain finance.

Trust in supply chain networks plays a moderating role in the relationship between blockchain technology and supply chain finance. The integration of blockchain technology in supply chain networks improves trust by enhancing the trust relationship between nodes and optimizing network topology performance [9]. The congruence and incongruence between blockchain and the norm of solidarity influence buyer–supplier trust, with technology uncertainty moderating this relationship [97]. Blockchain data technology is applied to trust management in supply chain finance systems to address the trust crisis caused by opportunistic behavior, and the coupling relationship between blockchain data and supply chain trust management is identified [98]. Digital supply chain finance based on blockchain technology offers advantages such as increased accessibility and affordability of financing options, while offline due diligence is still necessary in certain situations [99]. Trust-Chain, a trust management structure, utilizes blockchain technology to track supply chain member interactions and evaluate trust and notoriety ratings based on these interactions [100]. Consequently, based on the above arguments, it is proposed that:

H8 (b).

Trust in supply chain networks moderates the relationship between blockchain technology and supply chain finance.

The involvement of technology such as AI offers a variety of benefits to businesses, and smart contracts raise incorporation within the industry to increase payments without going into troublesome clerical procedures. Trust in supply chain networks plays a moderating role in the relationship between AI and supply chain finance [101]. Prior research found that trust positively mediates the relationship between supplier–buyer relationship and supply chain sustainability [102]. Additionally, alliance management capabilities, which are enhanced by AI-powered supply chain analytics, can increase an organization’s operational and financial performance [103]. This suggests that trust in supply chain networks can enhance the effectiveness of AI-powered supply chain analytics in improving supply chain finance [104]. The use of AI in supply chain finance can contribute to significant economic opportunities and improve the utilization of supply networks. Therefore, trust in supply chain networks is crucial in leveraging the potential of AI in supply chain finance and enhancing overall supply chain sustainability. Therefore, based on the above arguments, it is proposed that:

H8 (c).

Trust in supply chain networks moderates the relationship between artificial intelligence and supply chain finance

4. The Research Model

This paper suggests a unique conceptual model examining how trust moderates the impact of firm characteristics, artificial intelligence, and blockchain in improving supply-chain finance in Jordan. Moderators alter the nature of the relationship between two variables, whereas mediation serves as an intervening variable in the causal relationship in the supply-chain finance context [105]. Trust among supply chain partners determines the strength of the proposed relationship, such as the firm’s capabilities and resources, AI, and blockchain technology with supply chain finance. Figure 1 below indicates how blockchain technology mediates the relationship between (1) a firm’s capabilities and resources to supply chain finance and (2) artificial intelligence applied to supply chain finance. Similarly, the paper explores trust as a moderator to ascertain its effects on (1) a firm’s capabilities and resources in supply chain finance, (2) blockchain technology in supply chain finance, and (3) artificial intelligence in supply chain finance relationships.

5. Methodology

The researchers strategically selected targeted participants according to purposive criteria, ensuring relevant and adequate insights aligned with the study objectives. Purposive sampling involves selecting participants based on specific characteristics or criteria relevant to the research objectives [106]. Specifically, participants were mandated to hold pertinent industry expertise in domains such as supply chain management, finance, technology, or intricately linked sectors. The purpose of this criterion was to guarantee that participants were able to provide well-informed viewpoints and valuable insights into the subject of study. Furthermore, the study placed significant importance on the selection of participants who were actively engaged with or possessed expertise in emerging technologies, including artificial intelligence and blockchain. These technologies were considered essential elements of the investigation conducted in the study. Moreover, persons currently working in Jordanian startups or research institutions actively involved in the creation or application of innovative technologies in several domains were given priority. The inclusion of this contextual criterion was considered crucial to attain significant insights and views that are pertinent to the research subject. Therefore, to foster a thorough comprehension of the study setting, the inclusion criteria placed emphasis on a wide array of professional backgrounds. The study sought to obtain a comprehensive understanding of the intricate dynamics involved in the integration of supply chain finance and technology by involving specialists from diverse sectors such as technology, finance, and supply chain management.

Furthermore, this study used a combination of snowball sampling and purposive sampling methods because there was no specific sample frame for the intended respondents. Snowball sampling is a method that entails the initial identification of a select group of participants who satisfy the study’s inclusion criteria [106]. Subsequently, these individuals are requested to provide referrals for more potential participants who also satisfy the same criteria. This approach enables the discovery of supplementary responders by means of referrals from current participants, thus augmenting the sample size. The study sought to recruit respondents who fit the specified criteria by combining these two sampling procedures, even though there was no pre-defined sample frame. This methodology assisted the process of identifying and incorporating people who possess pertinent expertise, experience, and viewpoints that are essential for effectively addressing research inquiries.

Accordingly, the survey link, created using Google Forms, was disseminated to the target participants through email and various social media platforms, such as LinkedIn and Facebook. This multi-channel strategy was employed to broaden the survey’s reach and enhance its accessibility to potential respondents (see Appendix A). Each participant was required to provide prior consent before completing the questionnaire, ensuring compliance with ethical standards and data privacy regulations. As a result, a total of 362 completed questionnaires were received from respondents. After excluding 13 questionnaires due to missing data, the remaining 349 validated responses were utilized to analyze the proposed model and draw conclusions from the study. The necessary sample size was determined using G-Power software 3.1.9.7, with parameters set to 0.95 power, 0.05 alpha level, 0.15 effect size, and four predictors, resulting in a minimum requirement of 129 participants. In this study, the actual sample size (349) exceeded this minimum requirement of 129, alleviating concerns about sample size appropriateness.

This research adopts the questionnaire of firm capabilities and resources from the study of Carmeli and Tishler encompassing managerial capabilities, human capital—two items, auditing abilities—two items, organizational culture—four items, communication—two items, and firm reputation—two items [107]. For blockchain technology, from [84] were taken 14 items covering six of its important characteristics, namely transparency, tracing, authentication, security, unchangeability, and decentralization [83,108,109,110]. This study adopted the questionnaire developed by [111] as it explained the importance of artificial intelligence in the supply chain finance context to investigate the scope of AI in supply chain finance. The four-item scale for measuring supply chain finance was adopted from [112]. Trust is understood as a matter of honesty and benevolence of members and lies in a firm’s belief in its partners’ benevolence and honesty [113]. The study adopts a ten-item scale. The first five items evaluate the partner’s honesty, truthfulness, and reliability level, and the next five consider the firm’s interest or welfare. Ten experts validated the scale while the scale’s Cronbach’s alpha was 0.782. Figure 1 presents the study’s structural model.

Data Analysis Approach

As highlighted by [114], verifying data distribution is crucial, as certain analytical methods may be incompatible with non-normally distributed data. Utilizing Mardia’s multivariate normality test to assess multivariate data normality, the analysis indicated that both multivariate skewness (22.32) and kurtosis (175.18) exceeded the standard cut-off, confirming non-normality. Consequently, Partial Least Square-Structural Equation Modelling (PLS-SEM) proved ideal for evaluating data in this study due to superior reliability and robustness with small samples versus Covariance-Based SEM (CB-SEM), offering comparable statistical power [114]. This advantage is considered significant, particularly given the nascent nature of the AI, blockchain, and supply chain finance fields, which, in the context of the study, have received limited research and have a small pool of familiar users. However, despite these challenges, diligent efforts were undertaken to meet the requirements of PLS-SEM. However, researchers should still adhere to sample size recommendations provided by guidelines such as those outlined [115]. PLS-SEM also does not require the assumption of normally distributed data and is resilient to skewness (which is the case with this study) [116]. Additionally, PLS-SEM is capable of handling complex models with numerous observed variables, often avoiding convergence issues and maintaining robustness. For this study, SmartPLS software [117] was utilized to conduct the two phases of Structural Equation Modeling (SEM) analysis, encompassing measurement model and structural model assessments.

6. Preliminary Analysis

6.1. Common Method Analysis

Since data were collected from participants at the same time, Common Method Bias (CMB) may affect study results [118]. Hence, to reduce CMB impacts, the study included preventive measures. Specifically, the study included preventive methods to reduce CMB consequences. These measures included eliminating ambiguous and convoluted expressions, creating simple, concise, and easily understandable questions, protecting participants’ confidentiality, and encouraging honest responses [119]. In addition, statistical techniques were also employed to address CMB. In the beginning, an exploratory factor analysis was conducted using “Harman’s single-factor test” to assess all measurement items. Following the recommendations of [120], the outer model analysis algorithm was set to “PLS Regression”, which serves as the PLS-SEM equivalent of the exploratory factor analysis algorithm. Additionally, all indicators were assigned to a single latent variable. The dataset’s Average Variance Extracted (AVE), representing the total variance explained, was 0.382—below the 0.5 (50%) threshold, indicating no significant presence of common method bias (CMB). Furthermore, the Variance Inflation Factor (VIF) values for all constructs (see

Table 4. Measurement Model: Presents reliability and validity.

Items Measures	Factor Loadings	VIF	Cronbach Alpha	CR	AVE
FCR1	0.856	1.345	0.874	0.823	0.634
FCR2	0.754
FCR3	0.887
FCR4	0.763
FCR5	0.734
FCR6	0.773
FCR7	0.882
FCR8	0.783
FCR9	0.758
FCR10	0.867
FCR11	0.767
FCR12	0.758
AI1	0.788	1.501	0.819	0.881	0.649
AI2	0.743
AI3	0.778
AI4	0.854
BT1	0.763	2.103	0.831	0.817	0.692
BT2	0.824
BT3	0.762
BT4	0.812
BT5	0.767
BT6	0.853
BT7	0.789
BT8	0.711
BT9	0.795
BT10	0.819	1.672
BT11	0.772
BT12	0.734
BT13	0.824
BT14	0.752
SCF1	0.800	1.773	0.881	0.913	0.679
SCF2	0.868
SCF3	0.820
SCF4	0.885
TR1	0.706	2.114	0.897	0.925	0.712
TR2	0.888
TR3	0.871
TR4	0.884
TR5	0.779
TR6	0.827
TR7	0.840
TR8	0.739
TR9	0.856
TR10	0.774

Note: FCR = firm capabilities and resources, AI = artificial intelligence, BT = blockchain technology, SCF = supply chain finance, TR = trust.

) were below 3.3, confirming the absence of collinearity issues and reinforcing the conclusion that CMB is not a concern [121].

6.2. Descriptive Analysis

The descriptive data of the study show that the male participants represented 213 (61%), and the females were 136 (39%) of the total respondents. Regarding age, the respondents fell within four categories, 18–20 (67 respondents), 20–30 (137), 30–40 (112), and 40–50 (33 respondents). In terms of the educational background, 143 (41%) participants had completed a bachelor’s degree, 117 (33.5%) had completed a master’s level, and 89 (25.5%) had a Master of Philosophy or higher. Of the final respondents, 81 (23%) were CEOs, 64 (18%) were operation managers, 57 (16%) were consultants, 60 (17%) were IT managers, 48 (14%) were financial managers, and 39 (11%) were senior researchers.

7. Results

Following Hair et al. [114], we performed the analysis in two stages: applying a measurement model followed by applying a structural model.

7.1. Mesurmentl Model

Composite Reliability (CR) is based on the measurement model consisting of Factor Loading (FL), Cronbach’s alpha (CA), and Average Variance Extracted (AVE). To confirm the validity and reliability of data, the study performed several statistical tests following the suggestions of [121], who recommends calculating the factor loading, composite reliability, Cronbach’s alpha, and average variance extracted. The values for both CR and Cronbach’s alpha were found to be greater than the criteria of 0.70, confirming the reliability of all measurement constructs [122]. Likewise, the factor loadings and values of average variance extracted are determined to assure the convergent validity of the scale, and the resultant values were found greater than the criterion value of 0.70 for factor loading and 0.50 for AVE (see Table 4). Next, all the values of the Variance Inflation Factor (VIF) were found to have fallen below the maximum threshold value of 03 [123], which shows the absence of multi-collinearity issues in the data (see Table 4). Finally, to confirm the homoscedasticity, the study created a scatter plot. The examination of regression analysis of standardized residues of proposed relationships appears to be equally distanced from the regression line, revealing the fulfillment of the homoscedasticity presumption.

Following the indications of [122], a correlation matrix and square root of AVE were performed to assure the discriminant validity of the data (see

Table 5. Correlation matrix and the square root of the AVE.

Constructs	Mean	1	2	3	4	5
FCR		0.871 *
AI		0.528 **	0.889
BT		0.434	0.648	0.853
SCF		0.611	0.605	0.541	0.724
TR		0.679	0.761	0.525	0.671	0.856

* The diagonal values (in bold) contain the square root of AVE for each construct. ** The off-diagonal values contain correlations between constructs.

). The results reveal lower values of inter-construct correlations than their diagonal values of square roots of AVE, which confirms the presence of discriminant validity in the data.

In addition, the authors of [124] recommend determining the Heterotrait–Monotrait (HTMT) correlation ratio, which must be below the threshold of 0.85 to confirm the strong discriminant validity of the dataset (see

Table 6. HTMT ratio assessing discriminant validity.

Constructs	AI	SCF	TR	KS	BT
AI
CF	0.606
TR	0.600	0.651
Moderating 1	0.134	0.184	0.220
FCR	0.734	0.794	0.723	0.231
BT	0.745	0.577	0.585	0.113	0.744

).

7.2. Structural Model

After confirming the psychometric properties of the measurement model, the study examined the structural model(see Figure 2). The study evaluated the predictive capability of the structural model by calculating the R² values of dependent variables. The suggested model explained 43% of the variance in blockchain technology and 42% of the variance in supply chain finance, as this was greater than 30%, this shows the presence of a substantive and satisfactory model [125]. The study also measured the value of the Stone–Geisser Q² for blockchain technology to be 55.9% and for supply-chain finance to be 38.3%, both of which are greater than the threshold values [126]. This revealed the presence of predictive relevance in the results. To confirm the proposed relationships among the variables, the standardized values of path coefficients must be significant at a level of p < 0.05 [124]. The study results support all hypotheses (see

Table 7. Path coefficient and acceptance of the hypothesis.

Hypotheses	β	$\overline{\mathbf{x}}$	SD	T-Value	p-Values	Result
H1: FCR- > SCF	0.232	0.134	0.063	2.112	0.035 ***	supported
H2: AI- > SCF	0.273	0.137	0.075	4.048	0.001 **	supported
H3: FCR- > BT	0.247	0.246	0.054	4.509	0.005 *	supported
H4: AI- > BT	0.367	0.381	0.047	7.964	0.005 *	supported
H5: BT- > SCF	0.143	0.14	0.058	2.464	0.014 ***	supported
H6: FCR- > BT- > SCF	0.051	0.044	0.031	2.23	0.041 ***	Supported
H7: AI- > BT- > SCF	0.045	0.034	0.043	2.11	0.043 ***	Supported
H8a: FCRx TR- > SCF	0.289	0.168	0.048	3.48	0.001 **	supported
H8b: BT x*TR- > SCF	0.335	0.038	0.042	0.972	0.023 ***	supported
H8c: AI x*TR- > SCF	0.323	0.228	0.031	5.971	0.012 ***	supported

Note 1: AI = artificial intelligence, BT = blockchain technology, TR = trust, FCR = firm capabilities and resources, SCF = supply chain finance. Significance level *** p < 0.05, ** p < 0.01, * p < 0.001. Note 2: “x” is used to denote the product of two constructs, particularly when examining a moderating effect.Similarly, H3 and H4 are supported, as the analysis confirms a positive and significant relationship between firm capabilities and resources (β = 0.223) and artificial intelligence (β = 0.367) with blockchain technology. Together, these two variables explain 63.6% of the variance in blockchain technology (R² = 0.636), providing strong evidence for the acceptance of H3 and H4.

). Among the hypotheses, H1 confirmed a positive impact of firm capabilities and resources (β = 0.313) and artificial intelligence (β = 0.273) on supply chain finance. Together, firm capabilities, resources, and artificial intelligence contribute to 57.2% of the variance in explaining supply chain finance (R² = 0.572). H1 and H2 are thus accepted.

H5 is supported, confirming a positive and significant relationship between blockchain technology and supply chain finance in the context of Jordan (β = 0.143). Furthermore, the mediation analysis supports H6 and H7, confirming that blockchain technology (BT) plays a partial mediating role in the relationships between firm capabilities and resources (FCR), artificial intelligence (AI), and supply chain finance (SCF) (see

Table 8. Direct and indirect effects of the conceptual model.

Path	Indirect Effect	Direct Effect	Total Effect	VAF (%)	Mediation Type
FCR → BT → SCF	0.163	0.327	0.490	33.27%	Partial mediation
AI → BT → SCF	0.292	0.367	0.659	44.31%	Partial mediation

). Specifically, the variance accounted for (VAF) by the mediating effect of BT is 33.27% in the FCR to SCF relationship and 44.31% in the AI to SCF relationship. These findings indicate that while BT significantly contributes to enhancing SCF, both FCR and AI also exert notable direct effects. This underscores BT’s role as a critical, though not exclusive, mechanism in enabling SCF, highlighting the importance of integrating internal organizational capabilities with advanced technologies to achieve improved financial outcomes across supply chain networks.

H8a, b, c are supported, confirming that trust moderates the relationships between (a) firm capabilities and resources and supply chain finance (β = 0.289), (b) blockchain technology and supply chain finance (β = 0.335), and (c) artificial intelligence and supply chain finance (β = 0.323). These findings suggest that trust within the supply chain network plays a crucial role in facilitating the effective utilization of firm capabilities and the acceptance of technologies such as blockchain and AI in enabling financial transactions.

8. Discussion

This research extended evidence on supply chain finance using a sample of Jordanian firms. The study discussed the effect of firm capabilities and resources and artificial intelligence on supply chain finance through blockchain technology, assuming the substantial potential for improving supply chain financial practices in Jordanian firms. The findings confirm the positive impact of artificial intelligence and firm capabilities and resources and blockchain technology on supply chain finance (H1, 2, 3, 4, 5) are found consistent with existing studies, e.g., [21,127,128,129]. Supply chain finance is a capital approach that eases the financing terms between all members and lowers their financial risks. The firm’s capabilities and resources supporting the implementation of blockchain technology might be useful prerequisites for making supply chain finance decisions.

This implies that companies with stronger capabilities, resources, and utilization of AI tend to have better performance in managing their supply chain finances. Furthermore, utilizing blockchain technology can positively influence the efficiency and effectiveness of financial transactions within supply chains in Jordan.

The results also confirm the partial mediation between the proposed relationship (H6 and H7). This is consistent with prior research [72,84]. The mediation of blockchain technology confirms the authenticity, security, and accuracy of supply chain finance information, thus adding robustness and richness in managing supply chain relationships, particularly with financial partners. The study offers a model for Jordanian firms to overcome the problems in the traditional supply chain finance system. For example, there is ample evidence the supply chain finance sector is facing problems like redundancy delays, ineffective communication among supply chain members, and lack of trust between member firms. However, the model proposed in this research has the potential to address these supply chain finance problems in Jordanian firms. Therefore, the mediation indicates that leveraging blockchain technology in supply chain finance processes serves as a mechanism through which the effects of firm capabilities/resources and AI are transmitted. Blockchain enhances transparency, traceability, and security in supply chain transactions, potentially amplifying the positive impacts of firm capabilities/resources and AI on supply chain finance efficiency and effectiveness. Consequently, firms with stronger capabilities/resources and AI utilization can further optimize their financial processes by adopting blockchain solutions, leading to enhanced trust, reduced transaction costs, and improved overall performance.

Moreover, trust was found to be a moderator in the study, H8 (a, b, c), which is consistent with previous studies [130]. Therefore, the study believes that degrees of trust in the supply chain context have a greater improvement potential for the supply chain financing opportunities between partner firms and financial institutions. This implies that the level of trust among supply chain partners influences how firm capabilities/resources and technology adoption (blockchain and AI) impact supply-chain finance outcomes. Furthermore, trust acts as a facilitator that strengthens the linkages between firm capabilities/resources, technology adoption, and supply chain finance. Higher levels of trust enhance collaboration, information sharing, and risk-sharing among supply chain partners, thereby amplifying the positive impacts of firm capabilities/resources and technology adoption on supply chain finance performance. The study suggests that the firm’s capabilities and resources in integration with blockchain technology instigate mutual trust in the supply chain finance network and allow it to make secure and timely transactions that facilitate decentralization in the Jordanian firm’s context.

Blockchain technology provides a great opportunity for transforming SCF. Nevertheless, its scalability and performance characteristics should be censoriously addressed. Transaction speed in public blockchains may lead to slower transaction processing times especially proof of work compared to traditional financial systems [131]. In addition, the high energy consumption associated with certain blockchain protocols may raise concerns about environmental sustainability in its adoption in SCF [132]. Security is another hurdle where blockchain may experience vulnerabilities to cyberattacks when handling massive transactions [133].

Accordingly, the study highlights the interconnectedness of firm capabilities/resources, AI, blockchain technology, trust, and supply chain finance within the context of Jordan. These findings underscore the significance of technology adoption and trust-building initiatives in optimizing supply chain finance processes and driving business performance.

9. Implications

9.1. Theoretical Implications

The research made notable contributions to the existing literature by examining the link between firm capabilities and resources and supply chain finance, mediated by blockchain technology and trust. The research expanded the understanding of the role of firm capabilities, resources, and digitalization (AI and blockchain) in supply chain finance. It extends the understanding of the actual and potential uses of firm capabilities and resources and digitalization (e.g., artificial intelligence and blockchain technology) from the perspective of supply chain finance. This study analyzed the data and generated empirical findings. This paper provided a new understanding of the proposed viewpoints by contributing a holistic evaluation of the perspectives: firm capabilities and resources and digitalization: artificial intelligence and blockchain technology. It additionally offered comprehension of trust as a moderator having the potential to improve the application of modern technology and may encourage the firm to utilize its key capabilities and resources with supply chain members. This study answers the call for inquiries into the role of firm capabilities, resources, and AI in SCF considering blockchain technology and trust [14]. This paper contributes to the SCF and AI literature by understanding the mechanism through which firm capabilities and resources and artificial intelligence further contribute to the complex context of supply chain finance.

9.2. Practical Implications

The in-depth empirical analysis of the firm capabilities, resources, and AI in SCF considering blockchain technology and trust in this study allowed firms to consider their organizational strategies in their effort to keep high confidentiality and secrecy on their financial information. As proposed, the larger the trust level between supply chain partners the greater the opportunities for improving the SCF of the firm and may facilitate the execution of supply chain finance decisions. In terms of the practical application of AI and blockchain in supply chain finance, artificial intelligence plays a crucial role in transforming supply chain finance practices. For instance, companies like IBM have implemented AI-powered solutions to optimize working capital management by predicting cash flow needs more accurately. By leveraging AI algorithms, these systems analyze historical data, market trends, and supplier performance to provide real-time insights for better financial decision-making within the supply chain. Moreover, blockchain technology offers transparency, security, and efficiency in managing financial transactions within the supply chain. Companies like Maersk and IBM have collaborated on TradeLens, a blockchain-based platform that digitizes supply chain processes, enabling secure and transparent documentation exchange among stakeholders. Through smart contracts on the blockchain, TradeLens automates trade finance processes, reducing paperwork and streamlining transactions for improved supply chain financing. A notable case study is Walmart’s implementation of blockchain technology combined with AI in its food supply chain. By utilizing IBM’s Food Trust platform, Walmart tracks the journey of food products from farm to shelf using blockchain technology. AI algorithms analyze these data to enhance inventory management, reduce waste, and ensure product authenticity. This integration of AI and blockchain has revolutionized food traceability and safety practices within Walmart’s supply chain.

Finally, this research offers a useful assessment by demonstrating how organizations can enhance SCF through internal capabilities, AI, and blockchain. These tools improve transparency, reduce financing costs, and strengthen liquidity, especially for SMEs facing financial challenges. Beyond economic benefits, the societal implications presented the role of SCF in promoting financial inclusion and encouraging ethical, trust-based collaborations across global supply chains.

10. Limitations

Given the research aims and scope, this study has limitations that offer opportunities for future research. This study examined the role of firm capabilities, resources, and AI in SCF considering blockchain technology and trust. However, it does not address other perspectives, such as political strategies and firm policies. Similarly, the sample during the data collection process targeted startup firms in Jordan, future researchers are suggested to examine the model in other contexts. Also, the number of respondents (362) may not have been a proper representation of the target population.

Although the study provides empirical evidence from Jordan’s supply chain context, caution should be taken in generalizing the findings. Jordan’s unique socioeconomic indicators may affect the broader applicability of some results particularly those related to hypotheses that firm capabilities/resources positively influence blockchain technology (H3), blockchain technology positively influences supply chain finance (H5), and trust in supply chain networks affect how firm capabilities, blockchain technology, and other factors influence supply chain finance (H8), which are more sensitive to national-level development factors, market scale, or infrastructure capacity. These results may differ in countries with contrasting economic profiles, and further cross-country validation is recommended.

Similarly, as all the quantitative research data were collected online, there was an inevitable lack of environmental control and consistency for the respondents as they were completing the surveys. Additionally, due to the limited outcomes of this type of research, in-depth research is needed to address the gaps that this research was unable to address thoroughly. Moreover, this study did not consider the financial impact of implementing AI technologies, which presents an interesting area for future research.

11. Future Directions of Study

Potential avenues for future research could explore additional case studies, expand the investigation to other countries, and analyze the impact of specific blockchain applications on supply chain finance. Further exploration of the moderating role of trust in the adoption and use of emerging technologies in supply chain settings would also be beneficial.

Moreover, this research acknowledges the importance of considering additional potential sources of bias and confirms that numerical clustering analysis could provide valuable evidence to support the findings. Therefore, incorporating such analysis can enhance the robustness and generalizability of the results.

12. Conclusions

This study investigated the impact of firm capabilities/resources, artificial intelligence, blockchain technology, and trust on supply chain finance in the Jordanian context. The research objectives of this research were met through the confirmation of the positive impact of firm capabilities/resources and AI on supply chain finance, highlighting the importance of organizational capabilities in financial management within supply chains. Moreover, the identification of blockchain technology as a mediator between firm capabilities/resources, AI, and supply chain finance indicates the potential for leveraging organizational resources in implementing blockchain solutions for financial management and highlights the transformative potential of blockchain in optimizing financial transactions within supply chains. Furthermore, the establishment of trust as a moderator affecting the relationships between AI, firm capabilities/resources, blockchain technology, and supply chain finance in the Jordanian context emphasizes the importance of trust in shaping technology adoption and financial decision-making processes. This study has provided valuable insights into the interplay between firm capabilities/resources, AI, blockchain technology, trust, and supply chain finance. By confirming the hypotheses and research objectives, this research contributes to advancing our understanding of how technological innovations and organizational capabilities influence financial management practices within supply chains. The findings underscore the importance of leveraging innovative technologies like AI and blockchain while fostering trust among supply chain partners to enhance financial decision-making processes.