Blockchain-Driven Supply Chain Financing for SMEs in Eastern Europe

Abstract

Small and medium enterprises (SMEs) represent a fundamental pillar of economic development in Eastern Europe. Yet, they frequently encounter significant obstacles in accessing financing, stemming from informational asymmetries, elevated risks, the absence of collateral, and adverse regulatory environments. This research examines the primary determinants of adopting blockchain-based supply chain financing platforms, an alternative financing solution that streamlines processes, reduces costs, and enhances transparency and security. The study develops and validates an innovative conceptual model grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to a sample of 200 respondents across seven Eastern European countries, and the model’s hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The research findings demonstrate that supply chain partner readiness constitutes the most influential factor affecting behavioral intention to use blockchain-based supply chain financing platforms. Additionally, performance expectancy, effort expectancy, and perceived trust were identified as significant positive determinants. Furthermore, the study highlights blockchain readiness as a crucial factor influencing actual usage behavior. These findings provide valuable insights and contribute to advancing knowledge through the utilization of an extended UTAUT framework and validation of obtained results through comparison with other relevant studies in the field.

1. Introduction

Small and medium-sized companies (SMEs) are crucial components of vibrant business ecosystems and play a key role in promoting equitable and sustainable economic growth. In Eastern European countries: the Czech Republic, Hungary, Moldova, Poland, Romania, Slovakia, and Ukraine, SMEs contribute significantly to national economies, accounting for approximately two-thirds of total employment and generating around 60% of value added to the economy. These enterprises operate in critical sectors, including manufacturing, technology, and services, and contribute significantly to technological innovation in the region [1].

Despite their vital economic role, SMEs in Eastern Europe face substantial challenges due to current geopolitical tensions and global economic uncertainty. The ongoing conflict in Ukraine has created significant regional instability, leading to the closure of former export markets, unprecedented inflation, supply chain disruptions, and volatile exchange and interest rates [2]. Additionally, SMEs encounter persistent financing difficulties that limit their growth potential, stemming from information asymmetry, low capitalization, high failure rates, market vulnerability, elevated financing costs, and insufficient collateral to satisfy lenders’ risk requirements [3].

To address these financing challenges, Supply Chain Finance (SCF) has emerged as a viable alternative, optimizing working capital allocation and improving SMEs’ liquidity [4,5,6]. However, traditional SCF has limitations, including strong reliance on buyer creditworthiness, complexity, time-consuming processes, and prerequisites for established buyer–supplier relationships [7].

Blockchain technology offers promising solutions to traditional SCF limitations by enabling a decentralized, immutable ledger that supports automated transactions, enhanced transparency, real-time tracking, and reduced fraud risk. This technological foundation creates opportunities for safer and more effective financial operations within supply chain ecosystems [8,9,10]. The integration of artificial intelligence further enhances these capabilities through advanced credit scoring, risk assessment models, automated smart contracts, fraud detection, and predictive analytics for cash flow management.

A critical area of focus is the near-real-time adaptability that blockchain and AI technologies offer SMEs in response to market fluctuations. Recent research demonstrates that the combined implementation of blockchain and AI in supply-chain operations enhances data transparency and operational resilience, enabling fast, informed responses to dynamic business environments [11,12]. Additionally, this integration supports advanced risk-based financing models, allowing lenders and financial institutions to leverage reliable decentralized data for more accurate risk assessments and flexible financing tailored to SME profiles [13].

This study addresses these gaps by building UTAUT to develop a comprehensive conceptual model that examines factors influencing the adoption of blockchain-based supply chain financing platforms, including the role of AI technologies in enhancing user acceptance and platform usage.

This research paper is organized as follows: the introduction summarizes the goals and unmet research needs, which serves as a summary of the entire study. The second section includes a thorough literature review of financing opportunities and challenges for SMEs, traditional supply chain finance concepts, blockchain-driven solutions, and pertinent technology adoption models. The following section focuses on conceptual model design, including the development of research hypotheses and the identification of factors influencing the intention to use and the usage behavior of blockchain-driven supply chain financing platforms. The research methodology is then presented, followed by the analysis of the findings. General discussion, implications for theory and practice, limitations, and directions for further research are covered in the penultimate sections. The concluding section offers thorough findings and suggestions for researchers and practitioners in this developing field.

2. Literature Review

2.1. SMEs Access to Finance—A Growth Constraint

Small and medium-sized enterprises (SMEs) are vital drivers of economic growth, contributing to job creation, innovation, and income equality [14]. Global interest in SMEs has intensified since the 1980s, yet access to finance remains a significant obstacle. Approximately 70% of SMEs in emerging markets face credit constraints [15], challenges further exacerbated by the COVID-19 pandemic [16].

In Eastern Europe, despite significant transformation over two decades, SMEs’ growth potential remains constrained by financing difficulties [3,17,18]. Banks hesitate to lend to SMEs due to information asymmetry stemming from limited financial transparency and non-standardized statements [8], making it difficult to distinguish between viable and risky projects. Moral hazard arises because lenders cannot control how funds are used [19].

SMEs present a high risk due to low capitalization, high failure rates, and market vulnerability [20,21]. Insufficient collateral—resulting from a lack of consolidation or early-stage operations—prevents SMEs from meeting lenders’ requirements. Weak accounts receivable further limit access to financing [19,22,23].

Additional challenges include strict loan conditions, high financing costs—particularly in countries with inflation or currency fluctuations—and lack of experienced management [24,25]. Many owners lack knowledge of financial tools, leading to poor financial management and forcing reliance on alternative funding sources, such as personal savings, which become especially scarce during economic uncertainty [24,25].

2.2. Supply Chain Finance—An Alternative Financing Approach for SMEs

SCF has emerged as a key alternative financing mechanism for SMEs, building on early research in trade credit and operations management [26]. Its importance grew significantly after the 2008 financial crisis, when reduced bank lending created widespread liquidity gaps and prompted firms to seek innovative working capital solutions [27].

Although definitions vary, most scholars agree that SCF represents a coordinated set of financial practices designed to optimize cash flow and working capital across the value chain. From an inter-organizational perspective, SCF enhances supply-chain efficiency by enabling collaboration among buyers, suppliers, and financial institutions, thereby improving cash-flow visibility and resource allocation [28,29,30]. The Global Supply Chain Finance Forum further extends this view by emphasizing risk-mitigation practices and the use of digital platforms that increase transparency in trade flows [31].

SCF instruments primarily address liquidity needs related to accounts receivable and payable. Standard mechanisms include factoring and reverse factoring, forfeiting for long-term receivables, inventory-backed financing such as warehouse receipts, and advance payment financing. These tools allow suppliers to secure early payments while enabling buyers to optimize payment terms without disrupting operational continuity [32,33].

Effective SCF implementation yields multiple benefits for SMEs, including optimized cash flows, enhanced liquidity, reduced financing costs, and greater financial stability. Empirical studies show that improved working-capital management can significantly reduce financial constraints and positively influence firm performance [34,35]. Despite its advantages, SCF adoption still faces challenges. Information asymmetry remains a primary barrier, as lenders often lack sufficient real-time data to assess creditworthiness. Manual or paper-based processes increase operational risks and costs, while SMEs frequently struggle to meet stringent credit requirements. Additionally, fraudulent invoicing, duplicate financing, and broader cybersecurity risks continue to affect market confidence. Addressing these operational inefficiencies and enhancing data transparency are therefore essential for unlocking SCF’s full potential [34].

2.3. The Use of Blockchain Technology in the SME Financing Process

SCF systems are hindered by inefficiencies such as opaque processes and high transaction costs, which contribute to delays and eroded trust. Blockchain technology offers a transformative solution by enabling trusted value transfer through a decentralized ledger, enhancing transparency, efficiency, and security in SCF platforms [36].

The incorporation of blockchain into SCF represents a significant advancement in financial technology, with potential benefits including improved access to financing for Eastern European SMEs. By fostering a transparent environment for transactions among buyers, suppliers, and financial institutions, blockchain reduces information asymmetry and allows SMEs to demonstrate creditworthiness and supply chain integrity through comprehensive, auditable records [34,35].

Furthermore, blockchain platforms authenticate transactions through immutable digital ledgers with timestamping, enhancing data protection and reducing fraud. This trust-centric approach broadens financing opportunities and facilitates greater engagement from financial institutions, ultimately promoting sustainable development and reducing operational costs [26].

Blockchain platforms extend core enterprises’ creditworthiness throughout supply chains by implementing digital registration and transfer systems for bills and transactions. When core companies issue invoices, Tier 1 suppliers and retailers can subdivide and transfer these instruments to business partners. Smart contracts leverage sophisticated protocols to support, validate, and execute contract negotiations, guaranteeing fulfillment [26]. This integration enables automated payment execution, settlement, and financial reconciliation, reducing the risk of human intervention [37].

Blockchain adoption provides an efficient information infrastructure that enables smooth data flow, establishes comprehensive trust frameworks, and implements multi-stakeholder governance systems that address coordination requirements. Implementation improves transparency, optimizes information distribution, and promotes effective collaboration, ultimately enhancing overall SCF efficiency [36].

The focus on digital transformation has attracted significant research attention. Recent studies demonstrate blockchain’s applications: Gad et al. [38] improved transaction processing in trade finance, particularly digital letters of credit and factoring services. Du et al. [39] proposed innovative platforms utilizing blockchain to resolve trust issues while improving capital and information flow efficiency and achieving cost optimization. Chod et al. [40] applied analytical modeling demonstrating blockchain’s role in ensuring transparency, producing open-source protocols enabling scalable, cost-effective supply chain transparency and securing favorable financing conditions with reduced signaling costs.

SME-focused applications include developing blockchain-enabled platforms for automotive retail, providing reliable financing solutions that reduce expenses and accelerate cash flow [41]. Lycklama et al. [42] developed frameworks to address liquidity issues, eliminate double-financing risks, reduce information asymmetry, and improve the scalability of factoring services. Dong et al. [43] addressed purchase order financing difficulties by constructing three-tier supply chain models that apply game-theoretic methodology to evaluate the impact of blockchain-enabled deep-tier financing programs on optimal risk management and financial strategies, demonstrating that blockchain-facilitated transparency empowers informed financing decisions.

Table 1. Overview of blockchain-based supply chain financing platforms.

No	Platform	Description
1	AZHOS	AZHOS, a supply chain financing platform based in Liechtenstein, is revolutionizing the chemical supply chains by introducing a groundbreaking standard that combines blockchain technology with the synchronization of monetary and commodity flows. Leveraging IoT sensors, the platform accurately measures consumption and triggers automated orders. Consequently, payments are settled seamlessly, and receivables are autonomously financed. In essence, AZHOS streamlines the entire process of ordering goods, generating invoices, facilitating payment settlements, and funding assets [30,44].
2	Contour	Contour, previously known as Voltron, has launched a Corda blockchain-powered platform that aims to transform the lifecycle of letters of credit, enabling companies to optimize their bank credit lines for more intelligent business planning. By leveraging this platform, businesses can benefit from an efficient digital trade network for seamless import and export transactions. The initiative has already achieved significant milestones, conducting pilots in 14 countries that have successfully reduced the processing time for letters of credit from an average of 10 days to less than 24 h [45,46].
3	eTrade Connect	eTradeConnect, created by the Hong Kong Monetary Authority, is an innovative platform that primarily targets the Asian market. Its core objective is to optimize the efficiency of preparing and exchanging digital trade documents by leveraging distributed ledger technology (DLT). Moreover, it offers a streamlined process for businesses to request and obtain working capital from the banks involved. By utilizing this DLT-powered platform, customers and their trading partners can seamlessly participate in trade activities and secure trade financing, while sharing cost-effective information [47,48].
4	Halotrade	Halotrade represents a fintech startup that leverages blockchain technology to deliver sustainable supply chain financing solutions. The platform empowers buyers and financiers to systematically incentivize and reward environmentally responsible production practices, thereby fostering ecological accountability throughout the supply chain. By incorporating secure data channels and sophisticated transfer protocols, Halotrade guarantees transparency across all supply chain operations, resulting in enhanced trade efficiency, increased safety measures, and strengthened environmental stewardship [45,49].
5	Komgo	Komgo represents a fully decentralized commodity trade finance network built on the Quorum blockchain infrastructure, currently utilized by over 150 companies. The platform offers users three main product categories: digital trade finance solutions that streamline the submission of trade data and documents for financing, a KYC solution that simplifies and standardizes the Know Your Customer process while ensuring data privacy, and a certification feature that enables users to verify the authenticity of their documents within the network. By leveraging blockchain technology, Komgo is revolutionizing commodity trade finance, enhancing efficiency, security, and trust in the industry [50,51].
6	Minehub	Minehub is a digital platform that has partnered with IBM to introduce a groundbreaking global solution for mining and metals supply chains, harnessing the transformative potential of blockchain technology. By incorporating advanced technologies such as real-time tracking, automation, streamlined credit management, and digital transactions, Minehub brings a new level of transparency and traceability to commodity supply chains. The platform seamlessly supports every aspect of post-trade settlement processes, encompassing contracting, logistics, specifications, finance, and document management. Minehub has successfully engaged multinational corporations and micro, small, and medium-sized enterprises across different continents [51,52].
7	Skuchain	Skuchain leverages blockchain technology to deliver comprehensive solutions for supply chains and trade finance operations. The digital platform provides an Inventory Control & Finance solution that enables enterprises to access supply chain financing via the Distributed Ledger Payment Commitment, a global standard for payment commitments in blockchain networks. The platform’s Transaction Manager employs smart contracts called Brackets to digitize traditional trade finance documents and ensure their accessibility on the distributed ledger. Skuchain maintains collaborative partnerships with numerous major enterprises and their banking partners across multiple industries, including mining and minerals, electronics, automotive, and apparel. Their operational footprint extends across Africa, Asia, Europe, South America, and the United States [50,53].
8	Trade Finance Market	Trade Finance Market, a Singapore-based platform, specializes in providing receivables and invoice finance instruments with maturities of up to 120 days. The platform targets small and medium-sized enterprises (SMEs) in emerging markets and focuses on facilitating liquidity in global trade involving commodities, raw materials, and finished goods. The Trade Finance Market provides SMEs with a platform to connect with institutional investors, trade finance funds, and family offices, enabling them to access the financial resources needed to meet their working capital requirements. Additionally, the platform utilizes blockchain-based solutions to ensure the integrity of transactions and collateral [47,54].
9	Tradewind Finance	Tradewind Finance is a supply chain financing platform that offers a comprehensive suite of trade finance products, integrating financing, credit protection, and collections. By collaborating with retailers and their supply chain partners, Tradewind Finance optimizes cash flow at each stage of the transaction. Their goal is to establish a streamlined payment workflow that minimizes risk for SMEs and their retail counterparts. In a strategic partnership, Tradewind Finance is working with Verity Solutions LLC, a fintech company that uses blockchain technology to facilitate trade across regions. Since its establishment in Germany back in 2000, the platform has built a strong global footprint with offices located in Bangladesh, Brazil, Bulgaria, China, Hong Kong SAR, Hungary, India, Pakistan, Peru, Turkey, UAE, and the USA [50,54].
10	TrustOne	TrustOne is a supply chain financing platform that addresses working capital challenges faced by borrowers and lenders in the SME sector by leveraging blockchain technology to minimize risk and streamline loan processing time. This platform facilitates secure information sharing, enabling efficient credit transmission and reducing financing costs for SMEs. TrustOne is owned by Parity TrustOne Solutions Private Limited and has been developed using proprietary technology from Hyper Chain blockchain platform [45,55].

Source: Author’s results.

provides an overview of the selected blockchain-enabled supply chain finance platforms, highlighting their core functionalities, operational scope, and relevance to SME-oriented financing models.

The platforms included in Table 1 were selected based on three criteria: (i) documented relevance in academic or industry literature on blockchain-enabled supply chain finance, (ii) the presence of clearly described technological architectures or financing workflows applicable to SMEs, even when the platforms operate globally rather than exclusively in Eastern Europe, and (iii) the availability of verifiable information on system design, functionalities, and adoption context.

It is important to note that one of the platforms (Case 3) is no longer operational; however, it remains included because it represents an early, widely cited prototype that illustrates key mechanisms of blockchain-based SCF solutions. Its inclusion supports conceptual comparability across cases and reflects its continued relevance in the scholarly discussion.

2.4. Evaluation of Selected Blockchain Applications in Supply Chain Finance

The subsequent subsections provide an in-depth examination of each selected platform, detailing its operational mechanisms, technological architecture, and relevance within models of supply chain financing tailored for small and medium-sized enterprises (SMEs). The selection of case studies was driven by the aim of capturing a diverse array of technological implementations that illustrate the capacity of blockchain technology to facilitate distinct configurations of supply chain finance. Rather than restricting the analysis to platforms exclusively serving SMEs in Eastern Europe, cases were chosen to represent a variety of architectural approaches, including permissioned versus public blockchains, intelligent contract automation, and tokenized financing models, alongside differing financing mechanisms, such as receivables financing, dynamic discounting, and trade documentation validation.

This diversity fosters a comprehensive understanding of how blockchain capabilities can mitigate the financing constraints faced by SMEs, including information asymmetry, collateral constraints, and delayed payment cycles, across a range of operational contexts. Consequently, the selected cases were chosen not merely for geographic alignment but also for their ability to exemplify specific design features, governance models, and financing workflows that are transferable across regional boundaries to SME ecosystems. This methodological approach enhances conceptual analysis by facilitating the identification of overarching mechanisms and challenges pertinent to the adoption of blockchain-enabled supply chain finance.

2.4.1. Contour Network

Contour, a renowned trade finance network that recently received the prestigious “Best DLT Platform for Trade Finance” award at Global Finance’s World’s Best Trade Finance and Supply Chain Finance Providers 2023 ceremony, achieved a significant milestone by finalizing the first fully digitalized trade transaction in the iron ore industry, conducted in Chinese Renminbi (RMB). The transaction involved Baosteel, a Chinese manufacturer of stainless steel, and Rio Tinto, an Anglo-Australian mining and metals company. The successful completion of the transaction was made possible with the support and involvement of Chinsay, essDOCS, DBS Bank, and Standard Chartered Bank. This achievement serves as a testament to the effectiveness and enhanced security that can be achieved in cross-border trade finance. The success of this paperless trade can be attributed to Contour’s collaboration with Chinsay and its ICP platform, which provides corporate workflows and data integration to foster streamlined cooperation between businesses. The issuance of the Letter of Credit (LC) and approval of critical contract terms were executed digitally, eliminating the need for any physical documentation. Furthermore, the utilization of essDOCS’ paperless trade solutions, which involve electronic documentation for managing the electronic bill of lading, resulted in a substantial decrease in document processing time. This not only facilitated seamless integration of the data flow but also effectively addressed the obstacles associated with conventional paper-based systems [46]. The simplified process flow for this transaction is depicted in Figure 1.

The diagram shows how decentralized ledgers, smart contracts, and real-time data flows connect buyers, suppliers, and financial institutions, enabling transparent information exchange, automated verification, and secure transaction processing. By reducing information asymmetry and improving traceability, this architecture enhances trust among participants and supports more efficient financing decisions for SMEs. The figure provides a visual foundation for understanding how blockchain functionalities integrate with supply chain finance mechanisms to improve liquidity, mitigate risks, and streamline operational processes.

2.4.2. Tradewind Finance

Tradewind Finance has recently closed a EUR 9 million credit facility for a Turkish copper producer. By employing an innovative approach as part of their overall funding strategy, Tradewind effectively addressed the client’s specific needs and provided them with immediate working capital to foster their growth. The copper producer faced several compliance requirements that posed a challenge for both the client and its existing financial partners in securing funding. However, developed a customized export finance solution that granted the client additional financial flexibility while also adhering to their internal policies. Under this arrangement, Tradewind purchased the company’s outstanding receivables and converted them into upfront cash. A simplified process flow prepared by the authors is depicted in Figure 2.

The model illustrates how performance expectancy, effort expectancy, supply-chain partner readiness, blockchain readiness, and trust collectively influence users’ behavioral intention to adopt blockchain-based SCF platforms. In line with established technology-adoption theories, behavioral intention is subsequently expected to predict actual usage behavior. The figure visually synthesizes the theoretical foundations of the study and clarifies the structural paths empirically tested in the subsequent analysis.

2.4.3. Technology Adoption Models

The academic landscape has witnessed the emergence of multiple theoretical frameworks and models designed to explain technology adoption patterns and subsequent usage behaviors [56]. Several theoretical frameworks have emerged to explain technology adoption patterns, including the Technology Acceptance Model, the Technology, Organization, and Environment Framework, Diffusion of Innovation Theory, and UTAUT. These frameworks primarily emphasize psychological and behavioral factors that influence technology acceptance. However, limitations exist within each model, primarily due to differences in terminology and the complexity of human behavior, highlighting the inadequacy of any single theory to fully explain behavioral phenomena [57,58].

In 2003, some authors [59] conducted a comprehensive evaluation of eight technology acceptance frameworks: Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), the combination form of TAM and TPB (C-TAM-TPB), Innovation Diffusion Theory (IDT), Model of PC Utilization (MPCU), Motivational Model (MM), and the Social Cognitive Theory (SCT). Their rigorous analysis involved testing thirty-two variables identified across these eight theoretical models, carefully evaluating their shared characteristics and unique contributions.

The UTAUT framework identifies three key determinants influencing behavioral intention toward technology adoption: performance expectancy, effort expectancy, and social influence. Performance expectancy reflects users’ perceptions of the benefits and performance enhancements that technology is expected to deliver. Effort expectancy concerns the perceived ease of using technology, while social influence examines interpersonal dynamics and social pressures that affect technology acceptance decisions [60]. Additionally, the model includes behavioral intention and facilitating conditions as crucial drivers of actual usage behavior. Behavioral intention signifies an individual’s commitment to technology use, whereas facilitating conditions refer to the organizational and technical support available for implementation [61]. The framework incorporates four moderating variables—age, gender, voluntariness of use, and experience—to enhance its predictive accuracy across different contexts.

Despite its strengths, the UTAUT model has limitations, as subsequent studies indicate that the moderating effects of age, experience, gender, and voluntariness may be statistically insignificant. Consequently, many researchers have opted to exclude these variables, relying instead on fundamental factors that more reliably explain patterns of technology acceptance [62,63]. Venkatesh et al. [59,61] note that many studies use only selected components of the UTAUT model, sometimes omitting moderating variables altogether. Therefore, while UTAUT demonstrates significant explanatory power, its appropriateness for specific applications warrants careful consideration of the research context and objectives [64].

3. Research Methodology

The methodological framework adopted in this study details the conceptual model, hypothesis development, data collection procedures, and analytical techniques employed to examine the determinants of blockchain-enabled supply chain finance adoption.

3.1. Design of the Conceptual Model and Hypothesis Development

This study presents a novel conceptual framework that extends the UTAUT to investigate the determinants of blockchain technology adoption in supply chain financing contexts. The proposed framework offers an innovative theoretical perspective by modifying and expanding upon the foundational UTAUT model. Moreover, it resolves earlier methodological constraints by eliminating moderating variables, including age, gender, experience, and voluntariness of use, which demonstrated statistical insignificance in previous empirical investigations.

The developed conceptual framework integrates elements from established technology adoption research, specifically tailored to the distinctive environment of supply chain financing applications. The model also incorporates the specific characteristics and requirements of the intended user population, namely small and medium-sized enterprises (SMEs) operating in Eastern European markets.

Consequently, the original UTAUT framework has been augmented by integrating three novel constructs: Supply Chain Partner Readiness, Perceived Trust, and Blockchain Readiness. These enhancements are designed to improve the model’s relevance and applicability within the supply chain financing sector. The visual representation of this enhanced model is illustrated in Figure 3.

The framework comprises seven key constructs: Behavioral Intention to Use (BIU), Usage Behavior (UB), Performance Expectancy (PE), Effort Expectancy (EE), Supply Chain Partner Readiness (SCPR), Perceived Trust (PT), and Blockchain Readiness (BR). These constructs are interconnected through four hypothesized relationships.

The constructs selected for this research, combined with the theoretical relationships established in existing technology adoption literature, provide the empirical foundation for the research hypotheses that will be detailed in subsequent sections.

Building on the theoretical foundations outlined in the literature review, the proposed hypotheses integrate the core determinants identified in technology-adoption research with the specific characteristics of blockchain-enabled supply chain finance. Prior studies rooted in UTAUT highlight the importance of performance expectancy and effort expectancy in shaping behavioral intention toward digital technologies [59,61]. At the same time, subsequent extensions of the model further reinforce their relevance [62,63]. In parallel, research on supply chain finance and blockchain adoption emphasizes the importance of ecosystem readiness, including both partner readiness and technological preparedness, as key factors that reduce uncertainty and facilitate implementation across supply-chain contexts [29,30]. Trust also plays a central role in decentralized financial systems, strengthening the intention to adopt technologies that rely on transparent, immutable, and verifiable data-sharing processes.

Drawing on these theoretical insights, the conceptual model posits that behavioral intention to use BIU is a function of perceived usefulness, ease of use, ecosystem readiness, and trust. Consistent with prior empirical studies, behavioral intention is expected to translate into actual usage behavior, completing the technology adoption pathway. These theoretical analyses lead to the formulation of the study’s hypotheses, presented below:

H1. 

Performance and Effort Expectations (PE & EE) positively influence the behavioral intention to use blockchain for supply chain financing (BIU).

H2. 

Ecosystem Readiness (SCPR & BR) positively influences the behavioral intention to use blockchain for supply chain financing (BIU).

H3. 

Perceived Trust (PT) positively influences the behavioral intention to use blockchain for supply chain financing (BIU).

H4. 

Behavioral Intention to Use (BIU) blockchain for supply chain financing is positively correlated with actual Usage Behavior (UB).

For testing H1, managers will be more willing to adopt blockchain if they perceive that the technology will improve their operational performance (PE) and if they consider it easy to use, without requiring excessive effort (EE). The more valuable and simpler the technology appears, the greater the intention to adopt it, consistent with prior UTAUT-based empirical findings [59,61]. Given the multidimensional nature of the UTAUT-based constructs, H1 is specified at the level of individual relationships tested in the structural model.

For testing H2, blockchain adoption depends on the readiness of supply chain partners (SCPR)—meaning their willingness and capacity to integrate the technology—as highlighted in studies emphasizing the role of ecosystem preparedness in emerging technology implementations [29,30]. It further depends on internal organizational readiness (BR), including the necessary resources, culture, and IT expertise, a factor demonstrated to be critical in blockchain adoption contexts [65,66]. Given the multidimensional nature of H2, the empirical analysis allows for the independent evaluation of each component of this hypothesis. Accordingly, statistical testing assesses the effects of SCPR and BR separately, allowing acceptance or rejection of each relationship based on its statistical significance. This approach ensures a transparent and rigorous interpretation of the empirical results while preserving the original conceptual formulation of the hypothesis.

H3 offers users and ecosystem participants greater confidence in blockchain technology, the more predisposed they are to adopt it. Trust reduces the uncertainty associated with a new technology and facilitates the decision to implement it, especially for those with limited technological experience, as demonstrated in empirical research on blockchain trust [67,68].

H4 stated intention to use blockchain is a reliable predictor of actual technology usage, aligning with previous findings in technology-adoption literature [59,63]. If stakeholders express the intention to implement blockchain, they are likely to proceed with its use in practice for supply chain financing.

3.2. Measurement Items

To evaluate the variables within the extended UTAUT (Unified Theory of Acceptance and Use of Technology) framework, a 22-item questionnaire was used. To facilitate respondents’ evaluation, the questionnaire items were scored on a Likert scale from 1 to 7, with 1 indicating “strongly disagree” and 7 indicating “strongly agree.” Respondents were encouraged to base their assessments on their personal knowledge and expertise. Additionally, they were informed that there were no definitive right or wrong answers and that their responses would be used exclusively for academic research.

Table 2. Constructs and Measurement items.

Constructs	Items	Sources
PE	PE1: Implementing blockchain technology in supply chain financing would enhance my company’s operational efficiency.PE2: Through the adoption of blockchain technology, my company could realize significant cost savings.PE3: Blockchain technology would prove beneficial in facilitating faster access to financing for my company.	[59,69,70,71,72,73]
EE	EE1: It would be easy for my business to learn how to run blockchain-based solutions.EE2: The use of supply chain finance platforms powered by blockchain technology would be clear and understandable.EE3: I expect supply chain financing solutions based on blockchain technology to be easy to use in my company.	[59,69,70,71,72,73]
SCPR	SCPR1: The implementation of blockchain technology within my company would necessitate backing from our business partners and service providers.SCPR2: The primary business partners of my company possess the technological and financial capabilities required for blockchain technology deployment.SCPR3: My company’s business partners acknowledge the value and potential of blockchain innovation.	[57,74,75,76,77]
PT	PT1: I consider blockchain-driven supply chain financing platforms to be dependable.PT2: I regard blockchain-based solutions for supply chain financing as credible.PT3: I would have confidence in blockchain’s ability to perform effectively, even without supervision.	[36,78,79]
BR	BR1: My company would be required to secure personnel possessing the appropriate expertise level to facilitate blockchain technology implementation.BR2: My organization possesses the financial resources necessary to support the implementation of blockchain-based supply chain financing platforms.BR3: My company maintains the requisite technical infrastructure for blockchain technology adoption.BR4: My company would need to enable access to service providers to support blockchain technology implementation	[80,81,82,83,84]
BIU	BIU1: In the near future, I expect my organization to adopt blockchain technology.BIU2: My business is in favor of supply chain financing through the use of blockchain-based solutions.BIU3: My business intends to finance its supply chain using blockchain-based solutions.	[59,61,85,86,87]
UB	UB1: I anticipate that in the future, my business will most likely use blockchain technology to secure funding.UB2: In the future, I expect my business to regularly use supply chain financing platforms based on blockchain technology.UB3: I anticipate that blockchain-driven supply chain financing will be preferred by my organization over conventional supply chain financing management techniques.	[61,88,89,90,91]

Source: Author’s results.

presents the set of constructs and corresponding measurement items, along with the educational sources from which these items were derived.

3.3. Sample and Data Collection

This research employed a cross-sectional survey administered to 200 managers, directors, and other eligible experts from SMEs in seven Eastern European countries: the Czech Republic, Hungary, Moldova, Poland, Romania, Slovakia, and Ukraine. The intended participants were individuals who generally possess an understanding of the complexities of managing financial relationships among stakeholders in the supply chain and are aware of innovative technologies for SMEs, including blockchain.

The survey was designed in Qualtrics and distributed via the Prolific platform between March and May 2023. After removing incomplete responses, 200 valid surveys were obtained. The survey participants were predominantly men (58.50%), and the majority were from Romania (26.50%), followed by Poland (19.50%) and Ukraine (16.50%). The participants are employed in various industries, with 21.00% working in Manufacturing, 17.50% in Information Technology, 16.50% in Retail, 14.50% in Construction, 14.00% in Transportation, Distribution, and Logistics, and 10.00% in Hospitality and Tourism. The remaining 6.50% of participants are working in other sectors, including healthcare, agriculture, and personal care services. In terms of industry experience, 49.00% had 10–15 years, 35.00% had 5–10 years, and 16.00% had over 15 years. The demographic details of the participants are presented in

Table 3. Demographic details of the surveyed participants.

Attributes	Value	Precent
Gender
Female	83	41.50%
Male	117	58.50%
Industry experience
>15 years	32	16.00%
10–15 years	98	49.00%
5–10 years	70	35.00%
Countries
Czech Republic	19	9.50%
Hungary	13	6.50%
Moldova	22	11.00%
Poland	39	19.50%
Romania	53	26.50%
Slovakia	21	10.50%
Ukraine	33	16.50%
Industry
Construction	29	14.50%
Hospitality & Tourism	20	10.00%
Information Technology	35	17.50%
Manufacturing	42	21.00%
Retail	33	16.50%
Transportation, Distribution, Logistics	28	14.00%
Other	13	6.50%

Source: Author’s results.

.

Finally, we conducted a full collinearity test to assess the potential presence of common method bias (CMB) in the collection of data through online surveys. The presence of CMB can artificially enhance or distort the relationship between external and internal factors with a single participant [70]. Ref. [92] proposed a practical method for detecting common method bias by examining the variance inflation factors (VIF). If a VIF exceeds 3.3, it suggests the presence of problematic collinearity and implies that the model may be influenced by common method bias. As depicted in

Table 4. Inner model—Collinearity statistics.

Relation	VIF
BIU → UB	1.939
BR → UB	1.939
EE → BIU	1.345
PE → BIU	2.161
PT → BIU	2.919
SCPR → BIU	2.704

Source: Author’s results.

, our findings indicate that all the inner model VIFs derived from the full collinearity test in the model are below 3.3. As a result, we can conclude that the model is free from any indications of common method bias.

To validate the proposed model, the PLS-SEM (Partial Least Squares Structural Equation Modeling) approach was utilized. Using SmartPLS 4.0, the survey data was subjected to PLS-SEM analysis to evaluate the model and test the hypotheses. Unlike linear regression, which may be constrained in accounting for measurement errors, SEM adopts a confirmatory approach to analyzing the structure of the phenomenon and provides more reliable insights into the patterns of multiple indicator variables. PLS-SEM was specifically selected for this study due to its ability to estimate causal models with theoretical foundations, making it a contemporary multivariate analytical method. Additionally, PLS-SEM is more appropriate for estimating the variance of the connections between dependent and independent variables, surpassing covariance-based structural equation modeling techniques [71,72]. Finally, this analytical approach has been successfully applied to analyze and validate technology adoption models in recent scholarly articles in the supply chain management field [93]. Therefore, the data underwent a two-stage analytical approach. In the first phase, the measurement model was examined to ensure its validity and reliability. Subsequently, the second stage involved hypothesis testing using a bootstrapping procedure.

4. Results

4.1. Measurement Model

To assess the reliability of all constructs, we computed and examined both composite reliability (CR) and Dijkstra–Henseler’s rho (rhoA). Each construct achieved CR values above 0.7, demonstrating that the variables used to measure it consistently and reliably capture its essence [94]. Furthermore, adequate internal consistency and reliability were established as both Cronbach’s alpha and Dijkstra–Henseler’s rho values exceeded the 0.7 benchmark [92]. The reliability of individual indicators was also assessed using factor loadings, following the standard that loadings must surpass 0.60 [95]. All factor loadings met or exceeded this criterion, ranging from 0.706 to 0.951.

Convergent validity of the constructs was examined through the average variance extracted (AVE). According to [93], AVE values must exceed 0.5. Our findings reveal that all constructs achieved substantial AVE scores between 0.699 and 0.875, indicating that no less than 69.9% of indicator variance can be accounted for by the corresponding latent construct. These results demonstrate strong convergent validity.

In further evaluating reliability and validity, we examined the variance inflation factor (VIF) to identify potential collinearity issues among constructs. According to established guidelines, VIF values must remain below 5.00 to confirm the absence of collinearity [96]. Our analysis revealed that all VIF values fell well below this threshold, offering compelling evidence that no collinearity exists among the examined variables.

Table 5. Convergent validity and reliability of constructs and their measuring items.

Construct/Item	Factor Loading	VIF	Cronbach’s Alpha	rho_a	CR	AVE
BIU			0.868	0.869	0.92	0.793
BIU1	0.927	3.458
BIU2	0.904	3.051
BIU3	0.838	1.775
BR			0.945	0.947	0.96	0.857
BR1	0.894	3.135
BR2	0.938	4.818
BR3	0.932	4.398
BR4	0.938	4.692
EE			0.908	0.909	0.942	0.845
EE1	0.922	3.465
EE2	0.897	2.447
EE3	0.938	3.851
PE			0.797	0.82	0.877	0.704
PE1	0.811	1.280
PE2	0.861	3.192
PE3	0.843	3.051
PT			0.928	0.934	0.954	0.875
PT1	0.94	4.273
PT2	0.914	3.008
PT3	0.951	4.516
SCPR			0.901	0.907	0.938	0.835
SCPR1	0.921	2.935
SCPR2	0.927	3.125
SCPR3	0.893	2.570
UB			0.777	0.777	0.873	0.699
UB1	0.893	3.534
UB2	0.894	3.569
UB3	0.706	1.189

Source: Author’s results.

presents the complete set of values for factor loadings, VIF, Cronbach’s alpha, rhoA, CR, and AVE.

Discriminant validity represents the extent to which a construct demonstrates genuine distinctiveness from other constructs in the structural model. We initially evaluated the discriminant validity of the constructs using the Fornell–Larcker criterion [93]. This criterion establishes that the variance shared between a construct and its indicators must surpass the variance shared among different constructs [95]. Our findings demonstrated that the square root of the average variance extracted (AVE) was greater than the inter-construct correlation values, thereby establishing the discriminant validity of the constructs.

Table 6. Discriminant Validity—Fornell–Larcker criterion.

	BIU	BR	EE	PE	PT	SCPR	UB
BIU	0.89
BR	0.696	0.926
EE	0.536	0.419	0.919
PE	0.71	0.668	0.443	0.839
PT	0.765	0.708	0.482	0.687	0.935
SCPR	0.754	0.653	0.413	0.675	0.767	0.914
UB	0.684	0.579	0.635	0.678	0.664	0.634	0.836

Source: Author’s results.

displays the discriminant validity results based on the Fornell–Larcker criteria.

To provide additional validation of discriminant validity across all latent variables in the model, we applied the heterotrait–monotrait (HTMT) ratio of correlations. Our analysis revealed that the HTMT ratios remained below 0.90, meeting the established benchmark. These results confirm that the reflective variables exhibit apparent distinctiveness from one another.

Table 7. Discriminant Validity—Heterotrait–monotrait (HTMT) ratio matrix.

	BIU	BR	EE	PE	PT	SCPR	UB
BIU
BR	0.766
EE	0.603	0.451
PE	0.816	0.733	0.506
PT	0.85	0.755	0.523	0.767
SCPR	0.85	0.702	0.455	0.764	0.836
UB	0.833	0.675	0.757	0.864	0.779	0.759

Source: Author’s results.

presents the discriminant validity assessment using the HTMT ratio criterion.

4.2. Structural Model

We evaluated the significance of path coefficients through a standard bootstrap procedure utilizing 5000 samples. Bootstrapping was applied because PLS-SEM requires a non-parametric resampling approach to obtain reliable significance estimates, especially when data do not meet normality assumptions. Furthermore, we examined the structural model using coefficients of determination (R²), effect size (f²), and predictive relevance coefficient (Q²). For hypothesis testing, we performed significance tests on the path coefficients. Following the criteria established by [97], path coefficients must achieve a “t-value” exceeding 1.645 at the 0.05 significance level or surpass 2 at the 0.01 significance level. Our structural model analysis revealed that the causal relationship between Performance Expectancy and Behavioral Intention to Use (BIU) was statistically significant at the 1% level (PE → BIU, β = 0.225, t-value = 4.529, p < 0.01), thereby supporting H1. The proposed positive relationship in H2 between Effort Expectancy (EE) and Behavioral Intention to Use (BIU) also proved significant (EE → BIU, β = 0.168, t-value = 3.413, p < 0.01).

Likewise, Perceived Trust (PT) significantly influenced Behavioral Intention to Use (BIU) (PT → BIU, β = 0.293, t-value = 4.484, p < 0.01), thereby confirming H4. Moreover, Supply Chain Partner Readiness (SCPR) exhibited significance (SI → INT, b = 0.075, t-value = 2.291, p < 0.01). Figure 4 illustrates the causal relationships within the structural equation model.

Table 8. Hypothesis Testing Results for Behavioral Intention to Use.

Hypothesis	β Coefficient	Standard Deviation	T Statistics	p Values	Decision
H1: PE → BIU	0.225	0.050	4.529	0.000	Supported
H1: EE → BIU	0.168	0.049	3.413	0.001	Supported
H2: SCPR → BIU	0.306	0.061	5.022	0.000	Supported
H2: BR → BIU	-	-	-	>0.005	Not supported
H3: PT → BIU	0.293	0.065	4.484	0.000	Supported

Note: The relationship BR → BIU was estimated in the structural model but did not reach statistical significance; therefore, it is reported as not supported. Source: Author’s results.

presents the significance of testing results for the path coefficient, along with the final determination regarding hypothesis acceptance or rejection. Although the hypotheses were conceptually formulated in Section 3, the PLS-SEM approach requires testing each latent construct through its individual structural paths. Therefore, Table 8 reports the results for each component relationship, indicating which parts of the proposed hypotheses are empirically supported or rejected.

The predictive value of the structural model was also evaluated using the coefficient of determination (R²), as shown in Figure 4. As a result, PE, EE, SCPR, and PT collectively account for 70.6% of the variance in the Behavioral Intention to Use (BIU) blockchain-based supply chain financing solutions. H1 is fully supported, while H2 is partially rejected: supply chain partner readiness significantly influences behavioral intention to use, whereas blockchain readiness does not exhibit a direct effect on behavioral intention. Also, 48.8% of the variance in the Usage Behavior (UB) is explained by Behavioral Intention to Use (BIU).

The F-Square (f²) statistic within the structural model represents the extent of influence that an independent construct exerts on a dependent construct. According to [98], f² values of 0.02, 0.15, and 0.35 correspond to small, medium, and large effect magnitudes, respectively. Our analysis yielded f² values spanning from 0.041 to 0.298, as shown in

Table 9. F-Square values.

Relation	F-Square
BIU → UB	0.298
BR → UB	0.041
EE → BIU	0.071
PE → BIU	0.082
PT → BIU	0.1
SCPR → BIU	0.118

Source: Author’s results.

.

Behavioral Intention to Use (BIU) demonstrated a medium effect magnitude on Usage Behavior (UB). At the same time, Blockchain Readiness (BR), Effort Expectancy (EE), Performance Expectancy (PE), Perceived Trust (PT), and Supply Chain Partner Readiness (SCPR) exhibited smaller effect magnitudes.

Finally, we conducted the predictive relevance test (Stone–Geisser’s Q²) to examine the predictive validity of our structural equation model [99]. Higher Q² values for dependent constructs signify greater predictive relevance of the model for those constructs [95]. Our dependent variables consist of Behavioral Intention to Use (BIU) and Usage Behavior (UB). We performed the blindfolding procedure to determine cross-validated redundancy values for BIU and UB, yielding Q²(BIU) = 0.694 and Q²(UB) = 0.543, as presented in

Table 10. Stone–Geisser’s Q² values.

Attribute	Q2 Predict
BIU	0.694
UB	0.543

Source: Author’s results.

. These results demonstrate substantial predictive relevance of the model for both dependent variables.

5. Discussion

This research has identified and examined the key determinants that affect both the intention and actual adoption of blockchain technology among SMEs, aiming to enhance their access to financing solutions. These results provide a solid foundation for supporting and advancing the implementation of blockchain technology in the supply chain finance sector.

Drawing on survey responses from 200 participants representing SMEs across 7 Eastern European nations, this investigation highlights the distinction between behavioral intention to use blockchain-based supply chain financing platforms and actual usage patterns. The PLS-SEM analysis yielded several meaningful insights. Initially, this research validated extending the UTAUT model with Supply Chain Partner Readiness (SCPR), Perceived Trust (PT), and Blockchain Readiness (BR), which were both theoretically sound and statistically robust within the context of blockchain-enabled supply chain financing. The selection of these four independent variables yielded a model with substantial explanatory power, as Effort Expectancy (EE), Performance Expectancy (PE), Perceived Trust (PT), and Supply Chain Partner Readiness (SCPR) accounted for 70.6% of the variance in Behavioral Intention to Use (BIU).

Supply Chain Partner Readiness (SCPR) emerged as the most significant factor influencing Behavioral Intention to Use (BIU) blockchain-based supply chain financing platforms. This discovery illuminates the crucial function of supply chain partners in determining blockchain platform adoption for financing applications. It also underscores the importance of their technological competencies and financial capabilities in facilitating this adoption journey. This result aligns with [100], who identified partner preparedness as the primary driver in blockchain technology adoption. Additionally, research by [57,63] similarly found that partner readiness substantially influences blockchain adoption intentions.

The results also confirm the meaningful relationship between Performance Expectancy and the intention to adopt blockchain technology, as documented in earlier empirical investigations [64,101,102,103,104]. Likewise, the effect of Effort Expectancy on blockchain technology adoption intention was significant, consistent with previous findings by [9,60]. Furthermore, this study validates the considerable influence of Perceived Trust on adoption intentions for blockchain-based supply chain financing solutions, consistent with earlier investigations [67,68,105,106,107,108].

Finally, the absence of a significant effect of blockchain readiness on behavioral intention partially rejects H2, suggesting that organizational preparedness alone does not immediately translate into adoption intention in SME contexts. The results demonstrate that Blockchain Readiness meaningfully affects actual Usage Behavior, supporting previous work by [65,66]. These findings suggest that organizations exhibiting greater preparedness regarding technical infrastructure, skilled workforce, service provider accessibility, and financial resources are more likely to achieve effective implementation and integration of blockchain technologies within their operational framework.

The findings of this study both confirm and extend prior research on blockchain adoption in supply chain finance. Consistent with existing literature, performance expectancy, effort expectancy, and trust remain essential drivers of behavioral intention [59,61,67]. However, a key contribution of this study lies in demonstrating that blockchain readiness not only influences adoption intention but also directly affects actual usage behavior. While previous studies largely conceptualize readiness as a pre-adoption condition [65,66], our results indicate that organizational preparedness continues to shape post-adoption outcomes, particularly in SME-oriented supply chain finance environments characterized by limited digital maturity and resource constraints.

5.1. Theoretical Implications

This study addresses the nascent research on blockchain technology adoption, particularly within supply chain financing in Eastern Europe, an area that remains underexplored. Drawing on the extended UTAUT framework and empirical data from small and medium enterprises (SMEs), we offer insights for scholars, industry practitioners, and policymakers. Our research identifies key determinants influencing blockchain adoption intentions in supply chain finance and analyzes user behavior during implementation.

From a theoretical perspective, this study extends existing technology adoption frameworks by highlighting the persistent role of blockchain readiness beyond the intention stage. Unlike prior models that position readiness-related constructs solely as antecedents of behavioral intention, the empirical evidence presented here suggests a direct linkage between blockchain readiness and usage behavior. This finding refines the application of UTAUT-based models in the context of emerging financial technologies. It underscores the importance of considering post-adoption dynamics when analyzing blockchain-enabled supply chain finance adoption.

Theoretically, this study enhances the original UTAUT model by incorporating three novel variables: Supply Chain Partner Readiness, Perceived Trust, and Blockchain Readiness. It represents the first empirical examination of blockchain-based financing platforms among SMEs in Eastern Europe. Using PLS-SEM, we elucidate how Performance Expectancy, Effort Expectancy, Supply Chain Partner Readiness, and Perceived Trust positively influence behavioral intentions toward these platforms, with Supply Chain Partner Readiness emerging as the most significant determinant. Additionally, Blockchain Readiness was a statistically significant predictor of actual usage behavior, reflecting necessary technological and resource capabilities for effective implementation.

5.2. Practical Implications

This research presents practical insights for SMEs in Eastern Europe aiming to implement blockchain-based supply chain financing platforms. It identifies key determinants of blockchain adoption, emphasizing Supply Chain Partner Readiness as a primary factor influencing financing accessibility. SMEs need to assess their partners’ preparedness for blockchain technology to enhance implementation strategies.

Perceived Trust emerges as the second most influential factor affecting the intention to adopt such platforms. To foster trust among employees and partners, SMEs should develop strategies that enhance understanding of blockchain functionality and establish robust data protection and transparent governance frameworks.

The variables of Performance Expectancy and Effort Expectancy also significantly impact behavioral intention. SMEs must recognize the benefits of blockchain technology, enabling faster, more cost-effective financing while ensuring a user-friendly platform design.

Additionally, Blockchain Readiness plays a crucial role in actual usage behavior, necessitating organizational support in terms of infrastructure, skilled personnel, and financial resources. This investigation provides SMEs with a framework to foster participation in blockchain-based supply chain financing networks, enabling more efficient financing solutions beneficial to their operations and partners.

5.3. Limitations and Future Direction

Beyond the identified theoretical contributions to the existing knowledge base and managerial implications, this study’s limitations should be recognized as opportunities for future research. First, this investigation employed a cross-sectional design, examining Behavioral Intention to Use and actual Usage Behavior of blockchain-based supply chain financing platforms at a singular time point. To overcome this constraint, researchers should consider conducting longitudinal studies, given that blockchain technology adoption is a dynamic, multifaceted process and the technology itself undergoes continuous evolution. Longitudinal approaches would enable researchers to capture temporal changes in adoption patterns, track the evolution of user perceptions, and better understand how external factors, such as regulatory changes, market conditions, and technological advancements, influence adoption decisions.

While this study establishes a foundation for future researchers examining factors influencing blockchain technology adoption, the empirical validation of the newly developed UTAUT model was conducted using a limited sample of SMEs within Eastern Europe. By offering innovative insights into variable integration that drive blockchain-based platform usage intentions, this research creates pathways for further investigation and model application across diverse contexts, enabling a deeper understanding of decision-making processes in emerging technology adoption. Consequently, extending the examination of the enhanced UTAUT model to other geographical regions, including developed Western economies, emerging Asian markets, and developing African nations, would enhance the generalizability of the proposed conceptual framework. Additionally, testing the model across different industry sectors such as manufacturing, retail, healthcare, and agriculture could reveal sector-specific adoption patterns and requirements.

Furthermore, the proposed factors within the extended UTAUT model cannot be deemed comprehensive. Additional organizational elements such as top management support, organizational culture, and change management capabilities; economic considerations including cost–benefit analysis, return on investment expectations, and financial risk assessment; technical aspects such as system compatibility, scalability requirements, and cybersecurity concerns; regulatory factors including compliance requirements, legal frameworks, and government policies; market-related variables such as competitive pressure, customer demands, and supplier requirements; and social factors including peer influence, industry norms, and stakeholder expectations form a complex array of considerations that organizations must navigate when adopting disruptive technologies. Therefore, researchers are encouraged to explore integrating these additional factors to enhance the model’s explanatory power and practical applicability. Future studies might also investigate the moderating effects of organizational size, industry type, technological maturity, and cultural dimensions on the relationships identified in this research.

6. Conclusions

SMEs in Eastern Europe face significant challenges securing financing due to factors such as insufficient collateral, limited capitalization, elevated financing costs, and restrictive lending criteria from traditional financial institutions. These barriers, combined with market competition, information asymmetries between borrowers and lenders, and underdeveloped capital markets, create substantial working capital constraints throughout supply chains. Additionally, SMEs often struggle with complex bureaucratic procedures, lengthy approval processes, and a lack of credit history documentation required by conventional banking systems. However, blockchain technology adoption offers potential solutions to circumvent limitations in traditional financing approaches. This technology can improve access to financing and establish trust within supply chains by providing transparent, immutable transaction records, reducing information asymmetries, and enabling automated contract execution. As a result, SMEs may access new growth opportunities, contributing to enhanced economic development and regional competitiveness in Eastern Europe.

Despite blockchain technology’s potential benefits, its adoption for supply chain financing among Eastern European SMEs remains limited due to technological complexity, implementation costs, regulatory uncertainty, and a lack of technical expertise. Therefore, this study’s primary objective was to identify and analyze the determinants of both behavioral intentions to use and actual use of blockchain-based supply chain financing platforms among Eastern European SMEs. This research proposed and empirically validated an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This investigation presents an integrated model that incorporates three novel variables: Supply Chain Partner Readiness, Perceived Trust, and Blockchain Readiness. The study contributes to understanding the dynamics of blockchain technology adoption in Eastern European SME contexts, addressing a gap in the current literature.

The empirical findings demonstrate the significant influence of Performance Expectancy, Effort Expectancy, Supply Chain Partner Readiness, and Perceived Trust on Behavioral Intention to Use blockchain-based supply chain financing platforms. Notably, Supply Chain Partner Readiness emerged as the strongest predictor of Behavioral Intention. Furthermore, the study identifies Blockchain Readiness as a determinant affecting actual usage behavior. These findings provide valuable insights and contribute to the existing knowledge base in supply chain financing literature. The implications extend across multiple stakeholder groups, including SME decision-makers seeking innovative financing solutions; supply chain participants such as financial institutions, suppliers, and logistics providers; policymakers developing supportive regulatory frameworks; technology practitioners designing implementation strategies; and academic researchers investigating emerging patterns of technology adoption.