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Article

The Impact of Blockchain Technology on Lean Supply Chain Management: Cross-Validation Through Big Data Analytics and Empirical Studies of U.S. Companies

by
Young Sik Cho
1,*,
Euisung Jung
2 and
Paul C. Hong
2
1
College of Business Administration, Gyeongsang National University, Jinju 52828, Republic of Korea
2
John B. and Lillian E. Neff College of Business and Innovation, The University of Toledo, Toledo, OH 43606, USA
*
Author to whom correspondence should be addressed.
Systems 2026, 14(1), 3; https://doi.org/10.3390/systems14010003
Submission received: 15 November 2025 / Revised: 14 December 2025 / Accepted: 16 December 2025 / Published: 19 December 2025
(This article belongs to the Special Issue Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0)
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Abstract

Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented.

1. Introduction

Lean supply chain management is the extension of implementing the Lean philosophy to the entire supply chain beyond the boundaries of a single company [1,2,3]. It is based on the principles of lean manufacturing, which are commonly associated with the Toyota Production System (TPS). Lean supply chain management focuses on minimizing waste and maximizing efficiency while delivering high-quality products or services to customers [4,5]. In supply chain networks, supplier-buyer relationships can be negatively impacted by three significant risks: supplier performance risk, supply disruption risk, and intellectual property risk [6,7,8]. Especially opportunistic behavior and information asymmetry frequently occur in supplier-buyer relationships, and these relational risks make it more difficult for companies to implement optimal lean practices across their supply chain networks [9,10].
These risks pose challenges and potential difficulties in managing the supplier-buyer relationship, ultimately affecting the overall efficiency and effectiveness of the supply chain [11]. In particular, when the supplier and buyer are located in different countries, additional trade documents, such as a letter of credit (L/C) and bill of lading, are necessary for third-party verification, accounting for approximately one-fifth of the shipping cost [12]. In terms of the Lean philosophy, the effort to manage these relationship risks and the extra cost incurred by third-party verification is far from an actual value-add activity and is therefore considered a “waste” to be eliminated [13,14]. Managing these relational risks and reducing “trade-related paperwork” are essential aspects of lean supply chain management [9,15]. Despite existing literature, a gap remains in understanding the systematic implementation of Lean practices across the supply chain. This study examines blockchain technology as a disruptive innovation, enabling the transition from conventional, relationship-based approaches to data-driven digital supply chain management. Blockchain technology (BT) can play a crucial role in effectively implementing Lean Supply Chain Management (SCM) by: (i) streamlining transaction settlement through a single distributed ledger, reducing time and costs, (ii) expediting transactions and shortening lead times with peer-to-peer direct transactions, and smart contracts, and (iii) enhancing transparency and deterring opportunistic behavior through tamper-proof records [16,17,18]. Consequently, blockchain is expected to serve as an efficient operational platform for implementing Lean SCM practices. Moreover, amid the recent global supply chain crisis triggered by factors such as the COVID-19 pandemic and the Ukrainian-Russian War, the digitalization of supply chain networks through blockchain emerges as an alternative to traditional, relationship-oriented supply chain management [19,20]. Despite its potential, the impact of applying blockchain technology to Lean SCM remains relatively unexplored in empirical research. This study aims to bridge this gap by addressing the research question: How does blockchain technology impact the implementation of lean principles in supply chain networks?
This study adopts an exploratory research approach. First, a research model is developed to illustrate the role of blockchain technology in Lean SCM in three theoretical aspects: (i) innovation diffusion theory, (ii) agent theory, and (iii) transaction cost economics. Hypotheses of this study are then derived based on these three theoretical backgrounds and various real-world cases. Next, in the context of SCM, we conduct Big Data analysis using the LDA (Latent Dirichlet Allocation) technique to explore key domains and essential industry practices related to blockchain technology. This Big Data-driven topic modeling also helps us develop reliable measurement metrics for studying blockchain in the SCM field. The hypotheses in this study are empirically evaluated by analyzing survey data from 219 companies that have adopted blockchain technology in the United States. Finally, we present the limitations of this study and suggest future research directions. Additionally, we discuss the theoretical and managerial implications of our research results.

2. Theoretical Backgrounds

2.1. Literature Review

Technically, blockchain can be classified as a specific type of database architecture that uses DLT (Distributed Ledger Technology) [10]. A blockchain is a chain of blocks linked together using cryptographic techniques, forming an immutable and tamper-resistant chain of information [16]. All transactions are updated in near real-time across all nodes, resulting in a single digital ledger accessed and shared by the entire network [21]. In essence, similar to how the Internet revolutionized the exchange of information, the decentralized, consensus-based, transparent, and immutable nature of BT is considered a disruptive technology that could revolutionize the way people trade goods and services [10,22,23]. Deloitte’s 2020 Global Blockchain Survey, which involved 1488 executives from 14 countries, including Germany, China, UAE, Israel, and the U.S., found that 88% of respondents believed BT to be broadly scalable, implying eventual acceptance as a mainstream technology [24]. Figure 1 illustrates the current use of BT across various business transactions based on a survey by Deloitte. Notably, 83% of respondents stated that their companies could lose a competitive advantage if they did not utilize BT, and 82% planned to hire employees with BT expertise within the following year. Additionally, a global blockchain survey conducted by PwC involving 600 executives from 15 countries, including India, Japan, Denmark, and the U.S., revealed that 84% of respondents reported their organizations being involved in BT [25].
As blockchain technology rapidly enhances transaction efficiency and value within supply chains, researchers have actively delved into its applications. Treiblmaier [17] proposed various theoretical frameworks applicable to SCM blockchain research, including network theory, the resource-based view, transaction cost economics, and the principal-agent perspective. Additionally, Dolgui et al. [26] introduced a computational model for BT-based smart contracts in supply chain networks. Theoretical advancements in blockchain have shown considerable progress, but empirical research on its impact on supply chain management remains limited. Several factors contribute to this research gap, with the most significant hurdle being the challenge of identifying reliable metrics to evaluate the implementation of blockchain in supply chain practices. Thus, one of this study’s key objectives is to develop dependable metrics that ensure content validity related to blockchain, particularly in the context of SCM. Consequently, alongside the qualitative literature review in this section, the methodology segment will conduct a Big Data analysis using the LDA topic modeling technique to identify broader patterns of blockchain practices in supply chain networks. Table 1 summarizes the key studies on applying blockchain technology to supply chain management. These studies can be organized into three streams of research based on their purposes and methodologies.

2.2. Theoretical Rationale

This study discusses the application of BT in lean SCM based on three theoretical backgrounds: (i) innovation diffusion theory, (ii) agent theory, and (iii) transaction cost economics. Innovation Diffusion Theory (Rogers [38]) helps us understand the causes and processes by which innovative ideas and technologies diffuse in social systems [39]. In Innovation Diffusion Theory (IDT), diffusion is conceptualized as the process by which an innovation is transmitted through specific channels between parties within a social system over time [40]. Rogers and Shoemaker [41] described five basic properties of an innovation that determine whether users reject or adopt it: compatibility, trialability, observability, complexity, and relative advantage [39]. From this IDT perspective, blockchain technology, as a disruptive innovation, is expected to rapidly spread through supply chain networks over time due to its relative advantages, high compatibility, low complexity, and outstanding observability associated with blockchain adoption.
Agent theory is also used to construct the theoretical framework of this study. In supply chain relationships, buyers are considered principals, and suppliers are considered agents. However, since buyers (i.e., principals) have incomplete information about the behavior of suppliers (i.e., agents), they incur additional costs, known as agency costs, to monitor and control the behavior of suppliers [42,43]. Hence, from an agency theory perspective, blockchain technology is expected to minimize agency costs between suppliers and buyers by reducing information asymmetry through a distributed and immutable ledger, thereby increasing transparency.
In addition, transaction cost economics (Williamson [44]) can help explain how transaction costs between organizations change with the adoption of blockchain [17,45]. In terms of transaction cost economics, BT can be considered an operating platform that reduces transaction costs by minimizing the need for intermediaries and improving the costs of verifying and enforcing transactions in supply chain networks through automated processes (e.g., smart contracts) [10,46]. Figure 2 illustrates the conceptualized research model in this study, including all hypotheses. The specific rationale for each hypothesis is given in the following section.

3. Hypotheses Development

3.1. Blockchain and Lean Supply Chain Management

Toyota Motor Company’s lean manufacturing philosophy, developed in the mid-1970s, focuses on minimizing various forms of waste, including overproduction, excess inventory, defects, unnecessary space, transportation, and waiting time [47,48,49]. Lean SCM is a concept that extends the lean philosophy from single-plant management to the entire supply chain network [3,50]. However, effectively operating an integrated lean system in a supply chain network is more difficult as different decision-makers across the supply chain may have other priorities [4,9]. Moreover, major participants in supply chain networks encounter potential risks, including a lack of transparency, unpredictability, unequal influence, and opportunistic behavior [51,52,53]. A structured platform facilitating integrated lean management throughout the supply chain network is essential to tackle these challenges. Blockchain technology holds the potential to serve as this crucial solution due to the following reasons.
Since supply chains are complex networks of data, logistics, relationships, and systems, even slight mistakes in processes can result in significant delays and distortions, known as the bullwhip effect. However, BT features a digital ledger updated with all transactions in near real-time and shared by all network entities [16]. The nature of BT allows all supply chain network members to monitor and oversee fraud and mistakes at any time. In addition, blockchain also features various consensus protocols such as Proof of Stake (e.g., Ethereum), Proof of Work (e.g., Bitcoin), Proof of Elapsed Time (e.g., Hyperledger Sawtooth), which determine how consensus is reached among network participants when adding new information to the blockchain [54]. These consensus protocols also enable tamper-proof transactions on blockchain networks [55].
Moreover, blockchain serves as a digital platform to execute smart contracts (i.e., self-verifying and autonomous contracts) among network entities once specific conditions (e.g., product arrival at the carrier) are met [26,56]. Smart contracts not only automatically determine the terms and rules of the contract but can also enforce these obligations [57]. For instance, Samsung and IBM collaborated on ADEPT (Autonomous Decentralized Peer-to-Peer Telemetry), which facilitates ordering and payment through smart contracts [58]. Smart contracts provide a precise and automated method for exchanging assets, currencies, or rights, thereby minimizing the expenses associated with third-party intermediaries, such as bankers and auditors. Implementing blockchain’s smart contract features results in decreased transaction fraud, faster transaction processing, and improved regulatory compliance, leading to optimized overall lead time [59]. Consequently, from the perspective of transaction cost economics, blockchain reduces unnecessary transaction expenses among supply chain entities.
In summary, blockchain technology enables all parties in supply chain networks to supervise errors and fraud in real-time via a distributed digital ledger. Further, blockchain technology shortens settlement and lead times through automated processes (e.g., smart contracts). Therefore, based on these arguments, we arrive at the following hypotheses.
H1a. 
The use of blockchain technology in networks has a positive impact on supplier lean practices.
H1b. 
The use of blockchain technology in networks has a positive impact on buyer lean practices.
Previous research has empirically demonstrated that companies must be able to reconfigure appropriate implementation practices (e.g., sharing information with buyers, collaborating with suppliers, and using advanced manufacturing technologies) to achieve Lean SCM goals [60,61]. BT systematically establishes trust among all network parties in supply chains, making the essential implementation of pull production more seamless. When a transaction is newly executed, it undergoes encryption within a new block that includes a cryptographic hash (a unique digital fingerprint for each transaction) and is connected to all prior transaction records. The SHA-256 (i.e., Secure Hash Algorithm with 256-bit output size) has an output of 32 bytes (usually represented as a 64-character hexadecimal string), which means there are 2256 possible digest values [62] (p. 7). When a transaction is registered in a blockchain, it is cryptographically linked to all previous transactions. To tamper with a particular transaction on a blockchain network, the hashes of all subsequent blocks must be decrypted, making tampering almost impossible [63,64]. Consequently, from an agency theory perspective, the tamper-resistant nature of blockchain technology can mitigate opportunistic behavior that often occurs between stakeholders in a supply chain, thereby reducing unnecessary transaction costs [18,65]. In short, in a BT-based supply chain network, all transaction records are transparently shared and immutably stored on the blockchain, creating an environment in which it is difficult for all network participants (e.g., suppliers, buyers, and focal firms) to promote opportunistic behavior [10,66].
In addition, BT enables focal firms to reduce agency costs by providing a transparent and auditable record of transactions, reducing the need for costly contractual safeguards and oversight intermediaries [17,43]. Each node in a blockchain has its own 30+ alphanumeric addresses; in blockchain networks, transactions occur between these addresses, and all transactions are visible through the network [16]. Consequently, BT is expected to help minimize costs and waste that do not add value to Lean SCM within the supply chain network. Additionally, in innovation diffusion theory, observability is defined as the degree to which others can verify the outcome of an innovation [39]. This observability is vital for understanding how users embrace new technology and how rapidly that innovation spreads [67]. From this innovation diffusion perspective, BT is expected to diffuse rapidly through supply chain networks over time due to the immediate observability of its results and the relative advantages associated with blockchain adoption. Therefore, in light of these arguments, we propose the following hypotheses for empirical investigation.
H2a. 
Blockchain-enabled supplier lean practices positively affect a focal firm’s lean practices.
H2b. 
Blockchain-enabled buyer lean practices positively affect a focal firm’s lean practices.

3.2. Blockchain-Enabled Lean SCM and Operational Performance

In the preceding sections, we explored how blockchain technology enhances the efficient implementation of lean practices in supply chain networks. Now, shifting the focus to the focal firm’s perspective, let us delve into how these blockchain-enabled lean practices impact operational performance, encompassing quality, cost, delivery, and flexibility.
Within the blockchain network, every transaction record is synchronized and shared across a unified digital ledger, reducing costs by eliminating the need for redundant efforts and time-consuming data management associated with individual duplications in conventional supply chain processes [68]. Additionally, smart contracts allow focal companies to reduce third-party service fees and oversight costs directly. As an illustration, blockchain’s smart contracts can replace the need for third-party Escrow services in specific transactions. These smart contracts can securely hold funds on their nodes, acting on behalf of participants until the predefined conditions are fully satisfied [69]. In short, blockchain can be an alternative for systematically reducing transaction costs between parties within a supply chain. Drawing on these assertions, we propose the following hypothesis.
H3a. 
Blockchain-enabled lean practices are positively related to cost savings.
BT in supply chain networks also helps to enhance product quality. Today, with the advancement of the IoT (Internet of Things), the state of each inventory unit can be shared among all supply chain parties as a real-time, immutable record on the blockchain network. So, if there is a defect in the product or the supply chain process, not only can all parties on the blockchain instantly share information about the defect, but all parties can transparently track the cause of such defects. From the agency theory perspective, product defects often arise as a result of the agent’s opportunistic behaviors (e.g., raw material deception, information distortion, concealment, process violation) based on an information asymmetry between agents (i.e., suppliers) and principals (i.e., buyers) [70]. On the other hand, in a blockchain network, the flow of information is transparent to both agents and principals and can be accessed by both parties at any time [17].
Consequently, blockchain can significantly contribute to mitigating information asymmetry in supply chain relationships, fundamentally solving many quality issues arising from the agency problem. For example, following multiple high-profile recalls, Dole Foods adopted BT to process all its vegetables through its partner IBM Food Trust, allowing customers to verify the fruit’s origin directly by scanning the codes farmers distributed [71]. Another example is that the Everledger company utilizes a blockchain ledger system to establish quality assurance for a million individual diamonds, helping jewelers enforce regulations that prohibit “blood diamond” trading [16]. These arguments lead to the following hypothesis.
H3b. 
Blockchain-enabled lean practices are positively related to product quality.
Blockchain can synchronize all transaction data between buyers, suppliers, and focal companies into an immutable digital ledger, which reduces potential disputes throughout the procurement lifecycle. Research shows that disputes over commodity invoices are commonplace and require an average of 44 days to resolve; however, blockchain can eliminate these causes of conflicts by nearly 90–95% [34]. From an agency theory perspective, this demonstrates that blockchain adoption offers a systematic alternative that fundamentally eliminates agency costs. Credit Suisse, a Swiss bank, has adopted blockchain technology to trade US-listed stocks, allowing participants to settle transactions without intermediaries and enabling same-day settlement, rather than the typically required two days [72]. Blockchain transactions also significantly reduce the time and effort needed for customs clearance and product provenance through smart contract-based verified proof transactions. To summarize, blockchain technology can expedite order fulfillment by minimizing delays from the point of sale to product delivery to customers. As such, the following hypothesis is suggested.
H3c. 
Blockchain-enabled lean practices are positively related to delivery capacity.
Lastly, to achieve PPC (Production Planning and Control), one of the essential functions of operations management, strategic and systemized management is required to synchronize global SCM [73]. Blockchain technology can help synchronize global supply chain operations by providing a systematic platform for real-time inventory measuring and accurate demand forecasting across the entire supply chain process. For example, in a blockchain-based supply chain network, all stakeholders can instantly share transaction records and analyze the movement path and duration of their products and services in real time. In this way, the blockchain-enabled platform enables all network participants to schedule more flexible flow shops from the planning phase of products and services [26,33]. The COVID-19 pandemic demonstrated that unexpected events can disrupt global supply chains at any time [74]. In this case, BT could serve as a more critical platform for accelerating supply chain resilience by providing participants with accurate near-real-time visibility of supply and demand conditions. This real-time data sharing will thus enable partners in the blockchain network to be more responsive to dynamic supply chain demands. Accordingly, we propose the following hypothesis.
H3d. 
Blockchain-enabled lean practices are positively related to operational flexibility.

4. Methodology

4.1. Big Data Analysis of Blockchain Practices in Supply Chains

In this study, we employ big data analysis to explore key blockchain topics and industry practices related to supply chain management (SCM). Specifically, we utilize topic modeling as a text-mining technique suitable for processing vast amounts of unstructured textual data and extracting keyword topics [75]. Topic modeling is a widely utilized text-mining tool that uncovers latent semantic structures within textual data. It is an effective method for summarizing and extracting topics from extensive document collections, such as social media posts, customer reviews, and news articles [76]. Researchers have developed several topic models, including Latent Semantic Indexing (LSA) [77], Probabilistic Latent Semantic Analysis (PLSA) [78], and Latent Dirichlet Allocation (LDA) [79]. For this study, we opted to apply the LDA topic model algorithm due to its widespread adoption and representation of hidden topics as probabilistic distributions over words linked by a shared Dirichlet prior [79].
The chosen methodology will enable us to identify and analyze prominent themes and patterns within blockchain use cases, particularly in the context of Lean SCM for manufacturing and service firms. By leveraging the power of big data analysis and topic modeling, we aim to gain valuable insights into how blockchain technology is applied in various supply chain scenarios and its potential to enhance operational efficiency and transparency.

4.2. LDA Topic Modeling of Blockchain Practices in Supply Chains

The dataset utilized for this study was obtained from the Dow Jones Factiva database, a comprehensive digital archive of global news content. To collect relevant articles, we searched using the subject “supply chain” and the keyword “blockchain.” A total of 3793 news articles were retrieved from over 100 different news sources. Details of the data sources for topic modeling are provided in Table 2.
To prepare the data for analysis, each article was parsed, and the header data, which contained metadata such as publication date and news source, was excluded, focusing solely on the news body content. The corpus was preprocessed using the Python (version 3.9) Natural Language Toolkit (NLTK), which involved tokenization, stop-word removal, stemming, lemmatization, lowercase transformation, and punctuation removal [80]. Tokenization divided the text input into a sequence of word tokens, stop-word removal eliminated common non-significant words, and stemming and lemmatization converted words to their root and dictionary forms.
After preprocessing, we constructed a document vector matrix to apply the LDA Mallet model, which enabled both LDA model estimation from a training corpus and inference of topic distributions on new, unseen documents using collapsed Gibbs sampling from MALLET. To determine the optimal number of topics, coherence scores were calculated for each specified number. The coherence score measures the quality of a topic by evaluating the degree of semantic similarity between the top words of each topic, aiding in distinguishing semantically interpretable topics from statistical artifacts. Based on the coherence score evaluation, we selected 13 topics as the optimal number for the LDA model. Figure 3 illustrates the steps involved in the text-mining process, specifically the topic modeling analysis.
To interpret the results, we constructed a network of words within the topic groups, with semantic labeling based on the influence of those words, as presented in Figure 4. The LDA topic modeling analysis revealed ten themes consistent with the blockchain practices reported in Deloitte’s 2020 Global Blockchain Survey. It is worth noting that some discrepancies between the two studies may arise from differences in the scope; this research study exclusively focuses on blockchain use in supply chains, while Deloitte’s survey covers general industrial applications of blockchain.

4.3. Measurement Scales

Given the relatively new status of blockchain research in business, the development of widely adopted and reliable measurement tools for assessing blockchain use cases in the context of SCM is still lacking. However, the industry has seen more practical research on blockchain use cases. For example, Deloitte LLP has conducted an annual global survey since 2018 to explore use cases and investment trends for blockchain technology. This international research involved executives and practitioners from various countries worldwide, including the UAE, Germany, Singapore, China, Switzerland, the U.S., South Africa, and more. The 2020 Global Blockchain Survey by Deloitte [24], previously as shown in Figure 1, provided valuable worldwide BT use case statistics from 1488 executives and practitioners in 14 countries. Considering the size, expertise, and longitudinal nature of the survey, which spanned more than three years (N = 1053 participants in 2018, N = 1386 in 2019, and N = 1488 in 2020), these Deloitte survey results are considered to possess content validity for assessing BT use cases.
Additionally, an exploratory study using Big Data analysis with the LDA topic modeling technique was performed to identify key BT use cases related to SCM, as described in the previous section. The results of this exploratory study were utilized to evaluate the construct validity of the measurement scale adopted in this study. In summary, the LDA algorithm was applied to conduct the topic modeling involving 3793 news articles collected from 100 news sources. The analysis derived a total of 13 blockchain topics (primary BT use cases in the context of SCM), including real-time data sharing, track and trace, record reconciliation, global trading access, logistics platform, data security, digital currency/payment, service innovation, data access, smart contract, sustainable implementation, risk mitigation, and forecasting. These topic modeling results were aligned with Deloitte LLP survey questions to identify 13 standard blockchain practices used in supply chain networks for the survey questionnaire. Through this process, we secured the construct validity of the measurement scale adopted in this study. To verify the reliability and validity of the developed measurement scale, we surveyed 219 US companies that had adopted blockchain technology. Ultimately, we were able to develop 13 blockchain measurement items through cross-validation using LDA topic modeling and a survey of US companies. Further details are provided in the following sections.
We asked survey participants in this study to rate the extent to which their company implemented each BT practice in their supply chains on a 5-point Likert scale. A detailed questionnaire on this is provided in Appendix A. For Lean SCM practices, we utilized a measurement tool developed by Shah and Ward [14]. Survey participants were asked to report the extent of lean practice implementation in their supply chain networks or projects using a 5-point Likert-type scale, ranging from 1 (no implementation) to 5 (complete implementation). To measure operational performance, we employed four widely adopted metrics: cost saving, product quality, delivery capacity, and operational flexibility [81,82,83]. Survey respondents were asked to rate their operating performance in terms of their relative position in their industry on a 5-point Likert-type scale (1 = low end of the industry, 2 = worse than industry average, 3 = average, 4 = better than average, 5 = superior). Additional information regarding the questionnaire can be found in Appendix B.

4.4. Control and Marker Variables

To account for external influences, we controlled for firm age, as older firms are more likely to have well-established management practices, including lean SCM [84,85]. The age of the firm was determined by calculating the number of years it has been in business [86]. Industry type was also considered a control variable, as differences in technology sensitivity by industry could impact BT adoption and diffusion rates [87,88]. The firm’s industry was classified based on 2-digit Standard Industry Classification (SIC) codes.
Additionally, to reduce the risk of Common Method Variance (CMV), a marker variable (Cronbach’s α = 0.816) measuring the severity of the respondent’s insomnia problem was included in the questionnaire [89]. It is generally known that the lower the correlation between a specific indicator variable and other variables used in the study, the greater the CMV prevention effect. For this reason, the insomnia variable was selected as a marker variable in this study, as theoretically, the insomnia variable is expected to have minimal correlations with other variables. Further details of this measurement are provided in Appendix C.

4.5. Data Collection

Data for the empirical examination of the study hypotheses were collected through an internet-based survey designed in reference to the study by Dillman et al. [90]. The survey was conducted by selecting target respondents in advance through a professional survey company. A total of 219 valid samples were obtained from blockchain practitioners, representing an 84% response rate. The survey was conducted among full-time managers and executives across the U.S. via email invitations with links to the web survey. The high response rate was attributed to respondents deciding whether to participate in the study through a preliminary yes-or-no question regarding their company’s use of BT before completing the full-scale survey. The interest in BT by business practitioners in the U.S. is also considered a crucial factor contributing to the high survey response rate. Table 3 summarizes sample profiles used for the final analysis in this study.

5. Data Analysis

5.1. Testing of Scale Validity

Various tests were conducted to ensure the reliability and validity of the measurements used in this study. First, the scale reliability was assessed by calculating Cronbach’s alpha coefficient for each variable, following the methods of Cronbach [91] and Nunnally [92]. The results revealed that Cronbach’s alphas for all variables ranged from 0.802 (cost savings) to 0.957 (blockchain practices in supply chain networks), surpassing the acceptable reliability criteria of 0.70 or higher (as shown in Appendix B). Next, the convergent validity of each factor (i.e., latent construct) was evaluated through Confirmatory Factor Analysis (CFA). A single multi-factorial model was developed, encompassing all latent constructs, and standardized factor loadings were computed. The CFA results, reported in Appendix B, indicated that the factor loading coefficients of measurement items ranged from 0.717 to 0.867, exceeding the suggested threshold of 0.50 [93] (p. 686).
Additionally, the convergent validity of each factor was examined by calculating the Average Variance Extracted (AVE) [94]. The AVE estimates for all factors ranged from 57.89% (cost saving) to 70.78% (operational flexibility), surpassing the minimum acceptable threshold of 50% [93] (p. 700). These results collectively provide empirical evidence supporting the construct validity of the measuring instruments used in this study. Detailed results of all survey item tests are provided in Appendix B. Table 4 presents the correlations between the study variables and the mean and standard deviation of each variable.

5.2. Testing of CMV and Goodness-of-Fit

The data used in this research were collected from individual respondents in each company, which could introduce the common method variance (CMV) [95]. To proactively address this potential issue, we included the Insomnia Severity Index, developed by Bastien et al. [89], as a marker variable in the survey questionnaires [96,97]. This insomnia variable was chosen because it was theoretically expected to have minimal correlations with other variables in this study [98]. After completing data collection, we conducted a common latent factor analysis as an ex post approach [95,99]. This analysis involved examining changes in structural parameters when a common latent factor was added to our measurement model. The results showed that adding the common latent factor did not significantly change the structural parameters, indicating that CMV was not a prevalent threat in our sample data.
Additionally, we evaluated the goodness-of-fit statistics for the model to assess how well it aligned with the observed data. The results indicated that our model met the desirable thresholds for each fit index, demonstrating a good fit between the model and the data. Detailed information about the goodness-of-fit test results is summarized in Table 5.

5.3. Testing of Hypotheses

We conducted structural equation modeling (SEM) analysis using IBM AMOS software (version 23) to examine the research hypotheses proposed in this study. The SEM results are presented in Figure 5, along with standardized path coefficients. Regarding the testing of hypotheses 1a and 1b, the SEM analysis revealed that the use of blockchain technology has a positive impact on the buyer’s lean practices (β = 0.758, t = 9.693, p < 0.001) and the supplier’s lean practices (β = 0.799, t = 10.053, p < 0.001), providing strong support for H1a and H1b. However, the test results indicated that blockchain adoption in supply chain networks does not have a direct impact on the focal firm’s lean practices (β = 0.080, t = 0.936, p = 0.349).
For hypotheses 2a and 2b, the SEM results showed that both the buyer’s lean practices (β = 0.404, t = 5.781, p < 0.001) and the supplier’s lean practices (β = 0.550, t = 6.752, p < 0.001) are significantly related to the focal firm’s lean practices, thereby supporting H2a and H2b.
Regarding hypotheses 3a, 3b, 3c, and 3d, the SEM analysis demonstrated that the focal firm’s lean practices significantly affect its operational performance in terms of quality (β = 0.844, t = 10.109, p < 0.001), operational flexibility (β = 0.788, t = 10.010, p < 0.001), delivery capacity (β = 0.812, t = 9.546, p < 0.001), and cost-saving (β = 0.917, t = 9.900, p < 0.001), fully supporting hypotheses 3a, 3b, 3c, and 3d.

5.4. Post Hoc Analysis

The SEM results revealed that the adoption of blockchain technology did not have a direct impact on the lean practices of focal firms (β = 0.080, t = 0.936, p = 0.349). However, it was found that the lean practices of suppliers and buyers were significantly related to blockchain technology as well as the lean practices of focal companies, as depicted in Figure 5. This finding suggests that the lean practices of suppliers and buyers may act as mediators in the relationship between blockchain technology and the lean practices of focal firms.
To verify the mediation effect, we conducted Sobel tests [100]. The z-value was calculated using the formula: z = x × y/SQRT(y2 × SEx2 + x2 × SEy2), where x represents the unstandardized regression coefficient between blockchain and supplier/buyer lean practices, y represents the unstandardized regression coefficient between supplier/buyer lean practices and focal firm lean practices, SEx is the standard error of x, and SEy is the standard error of y. The Sobel test results showed a z-value of 5.596 (SE = 0.080, p < 0.001) for the mediating effect of supplier lean practices and a z-value of 4.980 (SE = 0.062, p < 0.001) for the mediating effect of buyer lean practices. These findings suggest that the lean practices of both buyers and suppliers fully mediate the relationship between blockchain technology and the lean practices of the focal firm. Put simply, suppliers and buyers play a significant role in influencing the implementation of lean practices within supply chain networks by focal firms through the use of blockchain technology.

6. Discussion

6.1. Implications and Contributions of the Study

The present findings have some meaningful implications and contributions to both theory and practice. First, this study contributes to the supply chain management literature by defining the scope of blockchain practices in the context of SCM and developing a tool to measure such blockchain practices. BT is refining supply chain relationships involving focal companies, suppliers, and buyers [101,102]. However, the existing literature has not clearly defined the scope of blockchain utilization, particularly in the SCM domain. Specifically, as mentioned earlier, while the theoretical development of blockchain has made significant progress, empirical research remains limited due to a lack of reliable metrics to measure blockchain practices in supply chains. Therefore, the blockchain measurement tool developed in this study, through cross-validation research utilizing a big data-based topic modeling method and a survey of U.S. companies, is expected to contribute to the advancement of empirical research on various blockchain studies in the future.
Second, the results of this study suggest that blockchain can serve as an effective operational platform for implementing Lean SCM. In traditional supply chain networks, suppliers and buyers are typically separated, which limits interaction and effective communication. Accordingly, opportunistic behavior and information asymmetry frequently occur in supplier-buyer relationships. These relational risks make it highly challenging for companies to implement lean practices across their supply chains, extending beyond their own business boundaries. The results of this study suggest that BT can systematically reduce transaction costs as well as agency costs through features such as transaction transparency, traceability, encryption, and smart contracts. Moreover, as shown in Figure 6, blockchain technology has the effect of connecting all supply chain partners (i.e., focal firm, suppliers, and buyers) in real time [16,103]. This suggests that BT could be a disruptive innovation that enables a shift from traditional relationship-centric approaches to data-driven digital supply chain management [10,33,104]. The empirical results in this study also highlight the transformative role of blockchain practices in Lean SCM. The research findings suggest that blockchain practices have a direct impact on the lean implementation of suppliers and buyers, whereas they have an indirect impact on the focal firm’s lean implementation. In other words, the improved implementation of suppliers and buyers enables the focal firm to enhance multiple dimensions of operational performance, thereby supporting the various conceptual proposals of previous studies [12,103,105].
Third, this study explains to business practitioners what BT practices are and how they are associated with the broader context of SCM. Management must define and clarify critical tasks related to prudent blockchain implementations. In this study, Big Data analysis of blockchain identified the following key relevant practices in the context of SCM. When implementing BT practices, enterprises need to consider practical tasks that are categorized into front-end contexts (e.g., global trading access, logistics platform, smart contract, real-time data sharing, forecasting) and back-end processes (e.g., data access, data security, digital currency and payment, track and trace, record reconciliation, risk migration, service innovation, sustainable implementation). For example, in the front-end context, Standard Chartered Bank, AIC, and IBM have partnered to develop a multinational insurance policy based on blockchain technology, enabling all parties involved to share policy data and documents in real-time [106]. Another example of blockchain adoption in a back-end process is that Walmart Canada pioneered a distributed ledger technology (DLT) based on blockchain solutions to create an automated system for managing billing and payment among 70 third-party freight forwarders [107]. Before this blockchain-backed DLT solution, more than 70% of invoices were disputed; however, today, less than 1% of invoices are in disagreement, and these disputes are easily flagged and quickly resolved due to the transparency and traceability of blockchain [107].
Lastly, the empirical results of this study provide practical guidelines for implementing blockchain in SCM. Managers are exploring ways to leverage blockchain to improve the dynamics of relationships between suppliers and buyers within their supply chain networks. Our research findings empirically support the notion that blockchain-based supply chain networks enhance operational performance in terms of cost reduction, delivery capacity, product quality, and real-time flexibility [102,108,109]. These findings imply that blockchain can help organizations improve operational performance through digital transformation. In the post-pandemic era, digital transformation has become a significant trend, and discussions are actively underway on how to achieve various digital transformation goals by leveraging various technologies, such as the Internet of Things (IoT), 3D CAD, artificial intelligence, and distributed ledger technology [110,111]. This study demonstrates that leveraging blockchain can enhance operational performance and create shareholder value. Therefore, the empirical results of this study are expected to provide valuable guidance to management in making decisions related to resource allocation for achieving blockchain infrastructure investment and operational goals.

6.2. Limitations and Future Work

The results of this study make a significant contribution to both theory and practice by empirically demonstrating that blockchain can serve as a systematic platform for companies to seamlessly integrate and manage lean management practices across their supply chain networks. However, it is essential to acknowledge the limitations of this research when interpreting the results. Firstly, as only one respondent from each company participated in the survey, the study may be susceptible to CMV [95]. To address this concern, a marker variable was included in the study to mitigate the CMV threat [97], and a common latent factor analysis was conducted after data collection [99]. Although the results suggest that CMV was not a significant issue in our data, future researchers may obtain more reliable insights by involving multiple respondents from each business operation. Next, our research model and hypotheses on the impact of BT on lean supply chain management were built on a somewhat general level. Although extensive text-mining analysis and literature reviews have been applied to the exploratory nature of our study design, future research is encouraged to focus on more specific blockchain use cases or industry verticals. Finally, the sample data for this study were collected using a cross-sectional design. Therefore, future researchers could gain further insights and validate the findings of this study by exploring the evolutionary impact of blockchain on lean implementation across the supply chain ecosystem using a longitudinal design.

7. Conclusions

This study explored the impact of blockchain technology on Lean SCM. Based on big data from the past decade, we conducted LDA topic modeling to define the scope of blockchain practices in the context of SCM. Furthermore, we cross-validated the results of big data analysis and surveys to develop metrics that aid in the study of blockchain technology. An empirical study of 219 US companies that had actually adopted blockchain technology revealed that blockchain adoption significantly improved lean management practices within the supply chain. In addition, the results of this study empirically demonstrate that blockchain technology can enhance corporate operational performance, including cost savings, quality improvements, delivery capacity, and real-time flexibility. These results suggest that blockchain technology can serve as a platform for systematically integrating and managing lean management practices across the entire supply chain network, ultimately contributing to improved operational performance. In the post-pandemic era, digital technologies are driving further innovative change, breaking down organizational boundaries and offering new opportunities for value creation, while also strengthening cybersecurity and increasing public trust in dynamic information flows. Based on the findings of this study, future research is expected to delve deeper into the patterns of supply chain networks that evolve in tandem with technological advancements. We hope that the findings of this study will contribute to a more in-depth exploration of supply chain network patterns that evolve in tandem with technological advancements in future research.

Author Contributions

Conceptualization, Y.S.C.; methodology, Y.S.C. and E.J.; software, Y.S.C. and E.J.; validation, Y.S.C. and P.C.H.; formal analysis, Y.S.C. and E.J.; investigation, Y.S.C.; data curation, Y.S.C. and E.J.; writing—original draft preparation, Y.S.C.; writing—review and editing, P.C.H.; visualization, Y.S.C.; supervision, P.C.H.; project administration, Y.S.C.; funding acquisition, Y.S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was conducted under grant VA-202001 from the National FinTech Center (USA) at Morgan State University, sponsored by Ripple.

Institutional Review Board Statement

This study did not conduct any human-related experiments. It is based on big data analysis and the collection of online survey data regarding the use of blockchain technology in supply chain networks. The online survey was anonymous, and participation was completely voluntary.

Data Availability Statement

The original contributions presented in this study are included in the article. For further inquiries, please contact the corresponding author.

Acknowledgments

We would like to express our sincere gratitude to Ali Emdad and Sanjay Bapna of the National FinTech Center (USA) at Morgan State University for their constructive feedback and research assistance on earlier drafts of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BTBlockchain Technology
DLTDistributed Ledger Technology
LDALatent Dirichlet Allocation
SCMSupply Chain Management

Appendix A. Survey Metrics for Blockchain Practices in Supply Chains

  • Q1. Is your company adopting or at least participating in blockchain-based solutions to run your business?
  • YES □ NO □
  • Q2. If YES, please indicate the extent of implementation for each blockchain use practice in your company’s supply chain network or project (1 = no implementation, 2 = little implementation, 3 = some implementation, 4 = extensive implementation, 5 = complete implementation).
12345
Identity protection
Revenue sharing
Data/record reconciliation
Tokenized securities (equity, debt, and derivatives)
Digital currency
Data sharing (real-time access)
Time-stamping
Custody
Certification/Smart contract
Asset transfer
Asset protection (data security)
Track-and-trace
Electronic payments

Appendix B. Measure Items and CFA Results

Table A1. Blockchain Technology Practices.
Table A1. Blockchain Technology Practices.
ItemsLoading aS.E.t-Valuep-ValueAVE
Blockchain practices in supply chains (α = 0.957)
Identity protection0.8020.10711.813***63.42
Revenue sharing0.8510.09812.567***
Data/record reconciliation0.8360.10012.340***
Tokenized securities (equity, debt, and derivatives)0.7870.09611.585***
Digital currency0.6680.0969.796***
Data sharing (real-time access)0.717---
Time-stamping0.7600.10611.186***
Custody0.7710.09711.351***
Certification/Smart contract0.8440.10512.459***
Asset transfer0.8470.09812.513***
Asset protection (data security)0.8460.10212.498***
Track-and-trace0.8420.10012.427***
Electronic payments0.7570.10511.134***
N = 219; *** p < 0.001; Standard error was not estimated when the factor loading was set to a fixed value 1.0; a Standardized coefficients.
Table A2. Lean SCM Practices.
Table A2. Lean SCM Practices.
ItemsLoading aS.E.t-Valuep-ValueAVE
Supplier lean practices (α = 0.892)
We give our suppliers feedback on the quality and delivery performance.0.821---67.56
Our suppliers are involved in the new product/service development process.0.8520.06515.326***
Our key suppliers deliver to the plant/store on a Just-In-Time basis.0.8150.07214.345***
Our suppliers are committed to annual cost reductions.0.7990.07113.926***
Buyer lean practices (α = 0.867)
Our buyers/customers frequently share current and future demand information with the marketing department.0.844---69.13
Our buyers/customers are actively involved in current and future product/service offerings.0.8670.06416.175***
Our buyers/customers give us feedback on the quality and delivery performance.0.7810.07013.703***
Focal firm lean practices (α = 0.908)
We maintain excellent records of all equipment maintenance-related activities.0.720---62.82
Shop-floor employees lead product/process improvement efforts.0.8290.09012.233***
Extensive use of statistical techniques to reduce process variance.0.8120.09511.983***
Our employees practice setups to reduce the time required.0.8230.09112.150***
Equipment is grouped to produce a continuous flow of families of products0.8030.09411.846***
We use a “pull” production system.0.7630.08911.224***
N = 219; *** p < 0.001; Standard error was not estimated when the factor loading was set to a fixed value 1.0; a Standardized coefficients.
Table A3. Operational Performance.
Table A3. Operational Performance.
ItemsLoading aS.E.t-Valuep-ValueAVE
Cost savings (α = 0.802)
The unit cost of product/service0.748---57.89
Inventory turnover0.7860.09212.098***
Cycle time (from receipt of raw materials to shipment)0.7480.09411.430***
Product quality (α = 0.852)
Product/service features0.802---65.72
Product/service performance0.8130.07613.482***
Conformance to product/service specifications0.8170.07313.568***
Delivery capacity (α = 0.804)
Order fulfillment speed0.813---67.32
Delivery as promised0.8280.07214.124***
Operational flexibility (α = 0.828)
Flexibility to change the output volume0.864---70.78
Flexibility to change product/service mix0.8180.06714.533***
N = 219; *** p < 0.001; Standard error was not estimated when the factor loading was set to a fixed value 1.0; a Standardized coefficients.

Appendix C. Marker Variable for CMV Mitigation

Please rate the current severity of your insomnia problem.
NomeMildModerateSevereVery Severe
Difficulty falling asleep
Difficulty staying asleep
Very SatisfiedSatisfiedModeratelyDissatisfiedVery Dissatisfied
How satisfied are you with your current sleep patterns?

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Figure 1. Ranking of global blockchain use cases. N = 1488 companies in 14 countries; the sum of the percentages exceeds 100%, as each company may adopt more than one blockchain application. Source: Developed based on the Global Blockchain Survey [24] (p. 33) conducted by Deloitte in 2020.
Figure 1. Ranking of global blockchain use cases. N = 1488 companies in 14 countries; the sum of the percentages exceeds 100%, as each company may adopt more than one blockchain application. Source: Developed based on the Global Blockchain Survey [24] (p. 33) conducted by Deloitte in 2020.
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Figure 2. Research model with theoretical rationale.
Figure 2. Research model with theoretical rationale.
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Figure 3. Steps for LDA topic modeling analysis.
Figure 3. Steps for LDA topic modeling analysis.
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Figure 4. Topic modeling results on blockchain practices in supply chains.
Figure 4. Topic modeling results on blockchain practices in supply chains.
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Figure 5. Results of hypothesis testing using SEM analysis. N = 219, *** p < 0.001, the figure in the parentheses represents the t-value.
Figure 5. Results of hypothesis testing using SEM analysis. N = 219, *** p < 0.001, the figure in the parentheses represents the t-value.
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Figure 6. Relationship-based supply chains (left) vs. Blockchain-based supply networks (right).
Figure 6. Relationship-based supply chains (left) vs. Blockchain-based supply networks (right).
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Table 1. Three major streams of research on blockchain in supply chain management.
Table 1. Three major streams of research on blockchain in supply chain management.
Stream 1: Empirical Studies on Blockchain Adoption in Supply Chains
▪
Queiroz and Wamba [27]: Examined the blockchain technology (BT) adoption challenges in the supply chain and identified the main drivers in India and the USA
▪
Tsolakis et al. [28]: Studied the pathway of sustainability and data monetization in the context of AI and BT implementation.
▪
Zhao et al. [29]: Explored the relationship between Decentralized Autonomous Organization (DAO) and platform performance. Found that distributing rewards, allocating tasks, and providing information moderate the impact of DAOs on platform operational performance.
▪
Agi and Jha [30]: Presented a practical framework for adopting blockchain technology (BT) in the supply chain. Identified the relative advantages of BT and external pressures as critical enablers influencing BT adoption in the supply chain.
▪
Wamba et al. [31]: Explored the effects of BT adoption on supply chain outcomes. Empirical evidence supported that pressure from knowledge-sharing partners significantly affects BT adoption and that BT transparency positively impacts supply chain outcomes.
Stream 2: Conceptual Studies on Blockchain in Supply Chains
▪
Saberi et al. [18]: Aimed to understand the pros and cons of adopting BT in supply chain management. Barriers to BT adoption are conceptualized into four categories: external, technical, inter-organizational, and intra-organizational.
▪
Chang et al. [32]: Sought to understand the applicability of BT in the process of international business and trade. Conceptualized a BT-based international trade process model, specifically the letter of credit payment process.
▪
Schmidt and Wagner [10]: Conceptualized a theoretical framework for BT adoption in SCM from a transaction cost economics perspective. Argued that BT can improve transaction costs by reducing uncertainty and opportunistic behavior in SCM.
▪
Kshetri [33]: Conceptualized the role of BT in meeting the primary goals of SCM through blockchain case studies. Demonstrated how blockchain contributes to achieving speed, risk reduction, quality control, flexibility, cost reduction, dependability, and sustainability.
▪
Banerjee [34]: Explored how Enterprise Resource Planning (ERP) can improve supply chain operations with BT. Discussed how ERP and BT complement supply chain functions, bringing transparency, cost savings, and efficiency.
▪
Treiblmaier [17]: Proposed a comprehensive theoretical framework for BT research in SCM, incorporating resource-based view, transaction cost economics, network theory, and principal-agent theory.
Stream 3: Model-based Studies on Blockchain in Supply Chains
▪
Eltoukhy et al. [35]: Presented a model based on BT for managing resource allocation and vehicle routing in modular integrated construction projects.
▪
Dwivedi et al. [36]: Studied a dynamic capability model-based inquiry into retailers’ resistance to BT adaptation.
▪
Lohmer et al. [37]: Investigated the impact of BT on supply chain resilience through simulation analysis. Significant improvements in supply chain resilience in efficient blockchain-based collaboration were found.
▪
Dolgui et al. [26]: Presented a control method for designing BT-based smart contracts in SCM. Developed computational algorithms and models for smart contract design for BT.
Table 2. The data sources for text mining.
Table 2. The data sources for text mining.
PublisherNPublisherN
PR Newswire—All sources455Kabulpress.org12
Supply Chain Digital232Vietnam News Summary12
Dow Jones Newswires—All sources160Arab News (Saudi Arabia)11
The Cointelegraph130Contify Banking News11
Business Wire—All sources103Contify Life Science News11
iCrowdNewswire97Metal Bulletin News Alert Service11
Journal of Engineering75National Iraqi News Agency11
GlobeNewswire (U.S.)75The Economic Times—All sources11
M2 Presswire—All sources67South China Morning Post 11
ENP Newswire64Express Computer10
Benzinga.com63Metal Bulletin Daily10
CoinDesk.com63ForeignAffairs.co.nz10
MarketResearch.com (Abstracts)61Resources News (RWE) (Australia)10
Just-Style51Press Association—All sources10
Inbound Logistics50Ledger Insights10
WRBM—All sources38Arab Finance9
Contify Retail News31Emirates News Agency (WAM)9
Newsfile (Canada)29Contify Energy News8
Canada NewsWire28Indian Patent News8
Financial Times 28Blockchain.News8
Mondaq Business Briefing25Journal of Commerce Online8
FreightWaves.com25TechCircle (India)8
Platts—All sources25ThomasNet News (U.S.)8
Taiwan Economic Journal 25Australian Associated Press 8
Journal of Commerce—All sources23The Australian—All sources8
American Shipper21PaymentsSource—All sources8
ASX ComNews (Australia)21The Straits Times—All sources8
Khaleej Times (United Arab Emirates)21Syrian Arab News Agency8
Ma’an News Agency (Palestine)20TradeArabia (Bahrain)8
Kuwait Times20Chain Store Age7
China Daily—All sources19The Financial Express (Bangladesh)7
Theflyonthewall.com17Information Technology Newsweekly7
Mubasher17The Namibian7
Namibian Sun17The Observer (Uganda)7
The Wall Street Journal—All sources17PR.com (Press Releases) (U.S.)7
LogisticsMiddleEast.com16Tehran Times (Iran)7
Mmegi (Botswana)16DC Velocity7
Material Handling & Logistics16Algeria Press Service6
Blockchain Tech News15Maritime Gateway6
Hong Kong Shipping Gazette Daily Enews15CIOL (India)6
Kuwait News Agency (Kuna)15Queensland Country Life (Australia)6
Mehr News Agency (Iran)15Global Finance6
Accord Fintech—All sources15Inside Cybersecurity6
The Canadian Press—All sources15Industrial & Systems Engineering at Work6
Investment Weekly News14Industry Week6
Reuters—All sources14Mobile Payments Today6
Sourcing Journal14CE NAFTA 2.0-USMCA6
YourStory (India)13The Nigerian Observer6
Canada Stockwatch13Press Trust of India6
ACCESSWIRE12Other Sources948
Jordan News Agency (Petra)12Total Documents3794
N = number of documents related to blockchain practices in supply chain management.
Table 3. Sample profile.
Table 3. Sample profile.
CategoryN%
Age of Company
(business period)		1~10 (years)5424.66
11~20 3716.89
21~30 3616.44
31~40 2712.33
>406529.68
Size of Company
(number of employees)		1~10 3917.81
11~1003917.81
101~10005826.48
1001~10,0004420.09
>10,0003917.81
Age of Respondent18~296228.31
30~447031.96
45~607534.25
>60125.48
Industrial Classification
[2-digit SIC]		Manufacturing [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]5727.14
Service [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87]12158.62
Other 3215.24
Table 4. Correlation Metrix.
Table 4. Correlation Metrix.
Variable123456789MeanS.D.N
1.Blockchain technology a 2.6411.100219
2.Supplier lean practices0.723 ** 2.8291.106219
3.Buyer lean practices0.662 **0.831 ** 2.9871.140219
4.Focal firm lean practices0.775 **0.868 **0.809 ** 2.7671.063219
5.Cost savings0.566 **0.702 **0.674 **0.729 ** 3.0030.920219
6.Product quality 0.502 **0.614 **0.614 **0.688 **0.804 ** 3.2070.991219
7.Delivery capacity0.433 **0.571 **0.596 **0.624 **0.773 **0.772 ** 3.2961.019219
8.Operational flexibility0.545 **0.547 **0.623 **0.618 **0.708 **0.720 **0.751 ** 3.1270.997219
9.Firm age0.0890.148 *0.0760.1190.237 **0.184 **0.149 *0.161 * 3.0541.572219
10.Industry type−0.036−0.0030.0000.0120.0890.1050.0100.1030.058n/an/a209
** p < 0.01, * p < 0.05 (two-tailed), a Blockchain practices in supply chain networks, S.D. = Standard Deviation.
Table 5. Model fit statistics.
Table 5. Model fit statistics.
Fit IndexDesirable ThresholdModel
Chi-square (X2)1179.295
The degree of Freedom (d.f.)566
X2/d.f.<3.002.084
CFI>0.900.910
RMSEA<0.080.071
RMSEA 90% Confidence Interval0.065~0.076
PNFI>0.500.757
TLIClose to 1.000.900
N = 219.
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Cho, Y.S.; Jung, E.; Hong, P.C. The Impact of Blockchain Technology on Lean Supply Chain Management: Cross-Validation Through Big Data Analytics and Empirical Studies of U.S. Companies. Systems 2026, 14, 3. https://doi.org/10.3390/systems14010003

AMA Style

Cho YS, Jung E, Hong PC. The Impact of Blockchain Technology on Lean Supply Chain Management: Cross-Validation Through Big Data Analytics and Empirical Studies of U.S. Companies. Systems. 2026; 14(1):3. https://doi.org/10.3390/systems14010003

Chicago/Turabian Style

Cho, Young Sik, Euisung Jung, and Paul C. Hong. 2026. "The Impact of Blockchain Technology on Lean Supply Chain Management: Cross-Validation Through Big Data Analytics and Empirical Studies of U.S. Companies" Systems 14, no. 1: 3. https://doi.org/10.3390/systems14010003

APA Style

Cho, Y. S., Jung, E., & Hong, P. C. (2026). The Impact of Blockchain Technology on Lean Supply Chain Management: Cross-Validation Through Big Data Analytics and Empirical Studies of U.S. Companies. Systems, 14(1), 3. https://doi.org/10.3390/systems14010003

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