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Article

Blockchain-Driven Supply Chain Analytics and Sustainable Performance: Analysis Using PLS-SEM and ANFIS

by
Shervin Espahbod
1,
Arash Tashakkori
2,
Mahsa Mohsenibeigzadeh
3,
Mehrnaz Zarei
4,
Ghasem Golshan Arani
4,
Maria Dzikuć
5,* and
Maciej Dzikuć
5
1
Department of Management Science, Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada
2
School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA
3
Department of Management, College of Business, University of Central Florida, Orlando, FL 32816, USA
4
Department of Business Administration, Alfred Lerner College of Business and Economics, University of Delaware, Newark, DE 19716, USA
5
Faculty of Economics and Management, University of Zielona Góra, Licealna Street 9, 65-417 Zielona Góra, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6469; https://doi.org/10.3390/su16156469 (registering DOI)
Submission received: 20 May 2024 / Revised: 10 July 2024 / Accepted: 24 July 2024 / Published: 29 July 2024
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Abstract

:
This study investigated the impact of blockchain-driven supply chain analytics on the dimensions of lean, agile, resilient, green, and sustainable (LARGS) supply chain management, as well as supply chain innovation (SCI) and sustainable supply chain performance (SSCP). The research involved 262 managers and vice presidents of supply chains from large- and medium-sized manufacturing companies listed in the Tehran Stock Exchange. A hybrid approach utilizing structural equations modelling with partial least squares-structural equation modeling (PLS-SEM) and the adaptive neuro-fuzzy inference systems (ANFIS) technique was employed for data analysis. The findings demonstrated a significantly positive effect of blockchain-driven supply chain analytics on SCI, the LARGS supply chain, and SSCP. Additionally, SCI exhibited a significantly positive impact on the LARGS supply chain and SSCP. Moreover, the LARGS supply chain was shown to have a significantly positive influence on SSCP. Both SCI and the LARGS supply chain played positive and significant mediating roles in the impact of blockchain-driven supply chain analytics on SSCP. Furthermore, the LARGS supply chain also acted as a significant mediator in the effect of SCI on SSCP. Lastly, SCI had a positive and significant mediating role in the impact of blockchain-driven supply chain analytics on the LARGS supply chain. In conclusion, it can be inferred that blockchain-driven supply chain analytics contributes to the enhancement of SSCP through the facilitation of SCI and the promotion of LARGS supply chain principles.

1. Introduction

In today’s highly competitive business environment, companies are shifting their focus from individual organizational performance to supply chain performance [1,2]. This includes all activities in the chain that meet the end customer’s needs, such as product access, timely delivery, and inventory management. Improving supply chain performance goes beyond organizational boundaries and encompasses raw materials, components, intermediate products, and finished products [3]. By incorporating environmental management into product lifecycles, companies can reduce their ecological impact and enhance their overall supply chain performance [4].
Integrating environmental management with supply chain management (SCM) is key to building sustainable supply chains. Sharing information among supply chain members is crucial for coordinated efforts [5]. Sustainable SCM involves integrating environmental requirements into all stages of the product lifecycle, from design to recycling. The goal is to achieve energy and resource efficiency while enhancing supply chain performance. Organizations are increasingly focusing on improving their sustainable supply chain performance (SSCP) to gain a competitive advantage and increase their profitability [6]. Managers, especially in high-income countries, are exploring ways to enhance their organizational performance while supporting environmental sustainability [7]. The effective management of SSCP can prevent the wastage of financial, human, and natural resources, leading to improved energy consumption practices in industries.
In recent years, the adoption of blockchain technology (BT) in SCM has gained significant attention due to its potential to enhance transparency, traceability, and security in supply chain operations [8,9]. Blockchain-driven supply chain analytics refers to the use of BT to collect, store, and analyze supply chain data to improve decision making and operational efficiency [10]. This technology enables the real-time tracking of goods, reduces the risk of fraud, and enhances trust among supply chain partners [11]. Despite the potential benefits of blockchain-driven supply chain analytics, there is a gap in the literature regarding its impact on lean, agile, resilient, green, and sustainable (LARGS) supply chain practices. LARGS practices focus on reducing waste, increasing flexibility, enhancing risk management, and promoting environmental sustainability throughout the supply chain [12]. It is essential to explore how blockchain-driven supply chain analytics can support and enhance LARGS practices to improve overall SSCP. By leveraging BT, organizations can enhance the efficiency of lean practices by streamlining processes, reducing waste, and improving resource utilization, agile responses to market changes, and demand fluctuations, enhancing the resilience of supply chains by ensuring the traceability and authenticity of products and implementing green practices by enabling organizations to track and verify the sustainability credentials of their suppliers, products, and operations. Moreover, the literature lacks in-depth studies on the direct impact of blockchain-driven supply chain analytics on SSCP. Understanding how BT influences SSCP is crucial for organizations aiming to achieve sustainability goals while maintaining their competitiveness in the market. Additionally, there is limited research on the relationship between blockchain-driven supply chain analytics and supply chain innovation (SCI). SCI refers to the adoption of new technologies, processes, and strategies to improve supply chain efficiency, effectiveness, and competitiveness [13]. Furthermore, organizations can leverage blockchain-driven analytics to explore new business models, revenue streams, and value propositions that disrupt traditional supply chain practices.
Considering the importance of SSCP for companies and organizations, therefore, it is very important to identify the effective factors. Although utilizing variance-based approaches, like partial least squares-structural equation modeling (PLS-SEM), aids researchers in identifying the key drivers of an output variable, it disregards the potential for non-linear correlations [8]. Approaches based on artificial intelligence [9,10,11,12] such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), have the capability to handle nonlinearities and provide better explanations for non-linear relationships compared to linear techniques like structural equation modelling (SEM) and multiple regression. Hence, this study integrates SEM and ANFIS methods to clarify non-linear relationships, assess the significance of factors, and predict SSCP based on blockchain-driven supply chain analytics’ impacts on the LARGS supply chain and SCI. To our knowledge, the utilization of such combined analytical methods to elucidate the determinants of SSCP is unprecedented. Therefore, this study aims to investigate the impact of blockchain-driven supply chain analytics on the LARGS supply chain, as well as SCI and SSCP.

2. Research Hypothesis

2.1. LARGS and SSCP

It is important to remember that sustainability is a multidimensional concept with social, economic, and environmental aspects. The implementation of LARGS practices in supply chains can contribute to achieving sustainable supply chains in the following ways: lean practices, supply chain agility, supply chain resilience [13,14], green practices, and sustainability [6]. The LARGS SCM contributes to reducing production time and transportation time, improving the level of integration, and facilitating effective information sharing [15,16]. Using techniques such as value chain analysis to eliminate waste in the supply chain, the lean supply chain connects interdependent partners to eliminate all waste in the supply chain. As a result of its capability to respond to shocks or disruptions in the chain, the supply chain is resilient. “Resilient” paradigms are based on a philosophy of responding to unexpected disruptions in order to gain a competitive advantage [17,18]. Generally, the concept of “agility” refers to the ability to respond to changing volumes and types of demands. Business partners in SCs are able to react quickly to a changing market as a result of their agility. There is a term known as “green” SC that refers to a green mindset in SCs that takes into account the environment, emphasizing the selection and sourcing of green materials, green design, green manufacturing, green delivery, green consumption, and the management of green products at their end of life. In order to achieve a sustainable supply chain, companies along the supply chain need to manage their material, information, and capital flows, as well as cooperate with one another. These goals should be integrated into economic, environmental, and social development, as derived from the needs of customers and beneficiaries [19,20,21]. In order to make their supply chains more sustainable, organizations have to engage in intraorganizational activities, such as designing sustainable products and processes, as well as external activities, such as developing partnerships with suppliers and customers. Several studies conducted in the past have shown that LARGS plays a significant role in improving the performance of supply chains [22,23]. Due to this, it can be assumed that:
H1. 
LARGS supply chain practices are effective for SSCP.

2.2. SCI, LARGS, and SSCP

Innovation in the competitive world is not only necessary for the growth of organizations, but also for their survival [24]. New ideas and methods quickly replace previous methods and change has become a normal routine. Modern organizations require rapid and continuous innovation in products, services, technologies, and processes. Due to rapid changes and fierce competition, companies have no choice but to innovate [25]. Companies that cannot consistently launch innovative products and services are doomed to failure. Therefore, the ability to continuously innovate products, services, and work processes is critical for organizations. SCI is especially important in its various aspects, such as technical advancements in the supply chain and changes in products, processes, or services that improve the efficiency of the entire supply chain and increase customer satisfaction at the end of the process [26,27,28,29]. There has been research that shows the benefits of SCI in reducing risks, as well as improving flexibility by reorganizing resources and practices in order to prevent inflexibility. A significant role for SCI in risk management is its ability to provide opportunities for strengthening the capability of managing risks [30,31,32]. The SCI concept requires continuous changes in supply processes and practices, as well as arrangements, which provide a framework for planning, monitoring, forecasting, and replenishment that is necessary to ensure accurate, concrete, and quick decision making during times of crisis. This, in turn, contributes to strengthening and enabling companies to cope with (unexpected) shocks in a more effective manner. The results of the conducted research also show the role of chain innovation in improving SSCP [33]. Therefore, it is assumed that:
H2. 
SCI is effective for SSCP.
H3. 
SCI is effective for LARGS supply chain practices.
H4. 
LARGS supply chain practices mediate the effect of SCI on SSCP.

2.3. Blockchain-Driven Supply Chain Analytics, SCI, LARGS, and SSCP

In recent years, the integration of Information and Communication Technology (ICT) into supply chain (SC) management has demonstrated significant potential for enhancing operational efficiency through advanced information sharing and communication technologies [34,35,36]. Among these technologies, blockchain technology (BT) stands out as a transformative innovation due to its capabilities in creating decentralized, secure, and transparent records across supply chain networks [37]. This paper explores the theoretical underpinnings and empirical evidence supporting the impact of blockchain-driven supply chain analytics on SSCP, SCI, and LARGS SCM practices.
Blockchain technology is characterized by its ability to establish a distributed ledger that records transactions in a secure and immutable manner [37]. According to Khan et al. [38], a blockchain serves as an organizational capability that enhances SC integration by enabling the transparent tracking of products, secure information sharing, and improved transactional transparency. This capability is crucial in modern supply chains, where complexities and demands for visibility are increasing [39].
The evaluation of security implications when utilizing BT in SCs is crucial to maintaining the confidentiality, availability, and integrity of data throughout the supply chain process [40]. BT is known for its robust security features including decentralization, encryption, and immutability, making it a reliable method for storing and transmitting data securely. Nonetheless, it is imperative to identify and mitigate potential security risks that may emerge from integrating BT into SCs. One key security concern when leveraging BT in SCs is safeguarding the privacy of data, particularly due to the transparent and decentralized nature of the blockchain ledger [41]. Additionally, ensuring the security of smart contracts, which are self-executing contracts recorded on the BT, is essential in SC operations [42].
Empirical studies underscore the effectiveness of blockchain-driven supply chain analytics in various sectors, particularly in enhancing operational efficiencies and ensuring product traceability [43]. Saberi et al. [44] emphasized its role in integrating SC partners and enhancing supply chain visibility. This visibility not only improves operational efficiencies, but also supports sustainability initiatives by promoting transparency in environmental and social practices [45,46].
Building on the theoretical foundation and empirical evidence, the following hypotheses are proposed:
H5. 
Blockchain-driven supply chain analytics is effective for SSCP.
This hypothesis is supported by studies demonstrating that blockchains enhance transparency and traceability, thus contributing to sustainable practices within supply chains [40,47,48].
H6. 
Blockchain-driven supply chain analytics is effective for SCI.
The literature suggests that blockchain technology fosters innovation by streamlining processes, enhancing data security, and facilitating real-time information sharing among supply chain partners [38,40].
H7. 
Blockchain-driven supply chain analytics is effective for LARGS supply chain practices.
The integration of blockchain technology supports lean and agile practices by reducing inefficiencies and enhancing responsiveness to market changes, while its secure and transparent nature promotes green and sustainable initiatives [40,45].
H8. 
SCI plays a mediating role in the effect of blockchain-driven supply chain analytics on SSCP.
SCI acts as a mediator by translating the technological capabilities of blockchains into improved performance outcomes, including sustainability metrics [40,47].
H9. 
LARGS supply chain practices play a mediating role in the effect of blockchain-driven supply chain analytics on SSCP.
LARGS practices mediate by operationalizing blockchain capabilities into LARGS initiatives, thereby contributing to overall supply chain performance improvements [45,46].
H10. 
SCI plays a mediating role in the effect of blockchain-driven supply chain analytics on LARGS supply chain practices.
The adoption of blockchain technology catalyzes SCI, which, in turn, influences the adoption and effectiveness of LARGS practices, creating a synergistic effect on supply chain performance [38,45]. In conclusion, blockchain-driven supply chain analytics represents a pivotal technological advancement with substantial implications for enhancing supply chain performance across various dimensions. By leveraging its capabilities in transparency, security, and operational efficiency, organizations can achieve significant improvements in sustainable practices, innovation, and overall supply chain leanness, agility, resilience, greenness, and sustainability.
This paper aims to address this issue by clearly articulating its innovative aspects. In the theoretical literature, significant emphasis has been placed on blockchain-driven supply chain analytics and its impact on LARGS supply chains, as well as SCI. Despite this, empirical studies examining the specific effects of blockchain-driven supply chain analytics on SSCP, particularly through the mediating roles of LARGS supply chains and SCI, are sparse. To fill this gap, our study proposes a model that investigates the impact of blockchain-driven supply chain analytics on SSCP, with a focus on the mediating effects of LARGS supply chains and SCI. Figure 1 illustrates our conceptual model, which is derived from both the theoretical framework in the literature and our specific research objectives. To achieve our research goals, we employ a dual-method approach incorporating both linear methods, such as PLS-SEM, and nonlinear techniques like ANFIS to analyze the outcomes of interest. By integrating these methodologies and focusing on the mediating mechanisms of LARGS supply chains and SCI, our study seeks to contribute significantly to the understanding of how blockchain-driven supply chain analytics can enhance SSCP, thus offering a novel perspective in the field.

3. Research Methodology

In our study, we utilized PLS-SEM and ANFIS for data analysis. We first employed PLS-SEM to validate the proposed conceptual model and then used ANFIS to analyze the essential factors and non-linear relationships. The choice of PLS-SEM was driven by the exploratory nature of our research and its reliance on a sophisticated conceptual framework to address our research objectives, as outlined by Hair et al. [49]. The SmartPLS 3.0 software was employed for the PLS-SEM analysis, while the implementation of ANFIS was carried out using the MATLAB R2013a software (Processor—Intel Core i7, RAM—16 GB, and Operating System—Windows 10).

3.1. Sample and Data Collection

The study focused on the managers and vice presidents of supply chains from large- and medium-sized manufacturing companies listed on the Tehran Stock Exchange (TSE). The TSE was chosen, as it represents a diverse spectrum of industries crucial to Iran’s economy, including automobile manufacturing, food processing, and chemical and petrochemical production. These sectors were selected due to their significant contribution to the national GDP and their reliance on complex supply chain networks. From this pool, 200 companies with over 50 employees were randomly selected. Following outreach via phone calls to request participation, a total of 350 questionnaires were distributed to the supply chain managers and vice presidents of these companies. Ultimately, 262 completed questionnaires (75%) were returned. This robust response rate strengthens the reliability of the data collected and enhances the study’s ability to draw meaningful conclusions about SCM practices within the context of TSE-listed companies. To address potential biases, efforts were made to randomly select companies from the pool of eligible candidates and ensure a representative sample size. Additionally, the inclusion of a variety of industries such as the automobile, food, chemical, and petrochemical sectors helped to mitigate any industry-specific biases.

3.2. Data Collection Instruments

To measure blockchain-driven supply chain analytics, we used the questionnaire compiled by Kamble [40], which consists of 9 items. For the assessment of the LARGS supply chain, we employed the variables introduced by Anvari [50]. This questionnaire includes 14 items that gauge leanness (3 items), agility (3 items), resilience (3 items), greenness (2 items), and sustainability (3 items). Additionally, the questionnaire developed by Kwak et al. [30] was utilized to measure SCI, encompassing 6 items. To evaluate SSCP, we utilized the questionnaire designed by Gunasekaran et al. [51], comprising 8 items. On a Likert scale of 1–5, every item was rated, with 1 indicating complete disagreement and 5 indicating complete agreement.

4. Results

4.1. Validity and Reliability

In order to determine the reliability of the data, Cronbach’s alpha coefficient and composite reliability were used and factor loadings, average variance extracted (AVE), and the Fornell–Larker test were used to check validity. This change in calculation showed higher and better composite reliability values than Cronbach’s alpha ratio. The criterion of this index is the same as the Cronbach’s alpha coefficient for the internal consistency of the measurement model, i.e., 0.7 or higher. There were 0.6 and more loadings for each item in a confirmatory factor analysis, which indicated that the structure of the data was well defined [17]. According to Table 1, the coefficients of the factor loadings for the items of the variables were above 0.6, and as a result, the coefficients of the factor loadings were confirmed. More importantly, if the calculation of the factor loadings between the construct and its indicators led to values less than 0.6, those indicators (questions) had to be modified or removed from the model. A convergent validity check was conducted using the AVE method. It was recommended that AVE values be at least 0.5 by Fornell and Larcker [52], which means that the construct can explain at least 50% of the variance in its markers. A summary of the factor loadings, composite reliability, and AVEs of the variables are presented in Table 1. Based on the values of Table 1, we can conclude that the constructs had an adequate and appropriate level of reliability and convergent validity.
The Fornell–Larker index was used to check whether the constructs had discriminant validity when they were compared to each other. In order to meet these criteria, the square root of the AVE of a construct must be greater than the correlation between the construct and other constructs with respect to that construct. The second validity criterion, the square root of AVE, is the second validity criterion that was analysed using a correlation analysis. It is shown in Table 2.
According to Table 2, the square roots of the AVE values for each construct were more significant than the correlation coefficients, suggesting that all constructs had an adequate discriminant validity, as evident from Table 2.

4.2. Structural Model Testing

A conceptual model has been proposed that can be used to predict SSCP by the means of SEM. It was decided to estimate the model using the PLS method in accordance with the hypotheses. Further, for the purpose of determining the significance of path coefficients, the bootstrap method (with 500 subsamples) was utilized for calculating the t-values by comparing 500 subsamples with 500 samples. According to the tested model, Figure 2 illustrates how the variables are related to one another in the model. SCI, LARGS, and SSCP are positively affected by blockchain-driven supply chain analytics. SSCP is positively impacted by SCI, and LARGS has a significant performance. SSCP is positively impacted by LARGS SCM. The variables’ explained variances that are represented by the numbers inside the circles.
A summary of each variable’s path coefficients and explained variance is presented in Table 3.
According to Table 3, the effect of blockchain-driven supply chain analytics is positive and significant on SCI, LARGS SCM, and SSCP. The effect of SCI is positive and significant on LARGS SCM and SSCP. The effect of LARGS SCM is positive and significant on SSCP. In addition, 59% of the variance in SSCP, 63% of the variance in LARGS, and 23% of the variance in SCI are explained by the variables of the model. Table 4 reports indirect coefficients.
The effect of blockchain-driven supply chain analytics on SSCP is positively mediated by SCI and LARGS SCM, as shown in Table 4. SCI has a positive and significant mediating effect on SSCP through LARGS SCM. LARGS SCM is positively influenced by blockchain-driven supply chain analytics through SCI. The tested hypotheses are shown in Table 5.
As an evaluation of the credibility and effectiveness of the PLS model, the goodness of fit (GOF) index is commonly employed as a method for evaluating the credibility and effectiveness of the PLS model. It is an index that measures a model’s ability to predict endogenous variables, as well as its ability to predict overall variables. For the tested model in this study, the GOF value of 0.63 indicates a good fit.

4.3. ANFIS Results

Non-linear relationships within the data were managed through appropriate modeling techniques in the PLS-SEM and ANFIS framework. ANFIS combines the concepts of fuzzy logic and neural networks to create a powerful learning system that utilizes machine learning and artificial intelligence algorithms [53,54,55,56,57,58,59,60,61]. The MATLAB Fuzzy Inference Toolbox was used to execute ANFIS. The toolbox was then applied to a set of 262 factor scores obtained from the output of the PLS, which were randomly divided into a training set comprising 70% of the original 183 lots of data and a test set comprising 79 lots of data. The first step involved determining the input and output variables, which were blockchain-driven supply chain analytics, SCI, and LARGS SCM as input variables, and SSCP as the output variable. In the second step, a fuzzy inference system was generated. The fuzzification of the input variables was carried out using the genfis2 function in the MATLAB software [62]. This function utilized the fuzzy clustering technique for fuzzifying the variables, and their membership functions were normalized [63]. This third step involved the use of a set of fuzzy rules to train the input data with the help of the ANFIS training function, which is part of the MATLAB toolbox. The final step in the development of the ANFIS model was to generate a prediction function from the test data to predict SSCP based on the ANFIS model.
As depicted in Figure 3, the outcomes produced by ANFIS demonstrated intriguing non-linear relationships. The inclination of the importance level lines for each factor significantly influences the results. The research indicated that LARGS SCM holds the utmost significance for SSCP, with blockchain-driven supply chain analytics and SCI following in subsequent stages. To enhance the visualization of the connections between the inputs and outputs, three-dimensional plots of the ANFIS model were created. Figure 4 presents the associations between each pair of input variables and outputs. These three-dimensional plots reveal that the influence of each input variable on the output was contingent upon the other input variables. Regarding the paired plot of SCI and LARGS SCM, there appears to be no interactive relationship in the case of high values for LARGS SCM. Regarding the paired plot of LARGS SCM and blockchain-driven supply chain analytics, it was found that SSCM increased slightly when LARGS SCM increased in the case of low blockchain-driven supply chain analytics. Regarding the paired plot of SCI and blockchain-driven supply chain analytics, in the case of low SCI, SSCM increased slightly in the beginning and then at a steeper slope. In the case of two increased dimensions, SSCM increased accordingly.

5. Discussion

By using SEM-PLS, this study was able to provide a model for the effect of blockchain-driven supply chain analytics on SSCP along with the mediating roles that the LARGS supply chain and SCI play in the process. The results of the study showed that the proposed model could explain 59% of the variance in SSCP, 63% of the variance in LARGS, and 23% of the variance in SCI, with a reasonably good fit with the data. According to the results of model, this study not only demonstrated the impact of blockchain-driven supply chain analytics on SSCP, but also highlighted the mediating roles of the LARGS supply chain and SCI. The findings indicate that the proposed model accounted for a significant portion of the variances in SSCP, LARGS, and SCI, emphasizing the importance of these factors in driving sustainable supply chain practices. Furthermore, this study aligns with existing literature [40,45,46,47,48,64] by showcasing the positive effects of blockchain technologies on SCI, LARGS SCM, and overall SSCP. These findings underscore the potential for blockchain technology to enhance supply chain mapping, promote innovation, and improve sustainability within supply chains. The study illustrates how blockchain technologies facilitate the secure and reliable sharing of information among key supply chain stakeholders, leading to improved coordination, accelerated transactions, and an enhanced supply chain performance [34,65]. By leveraging blockchain-driven supply chain analytics, companies can establish a transparent and immutable system for data exchange, ultimately fostering collaboration and driving efficiencies within the supply chain ecosystem. Overall, the study emphasizes the transformative impact of blockchain technologies on SCM, highlighting their role in promoting transparency, collaboration, and sustainability. By embracing blockchain-driven solutions, organizations can create an open manufacturing system that empowers stakeholders to share information and their expertise, ultimately driving performance improvements across the supply chain.
The findings of the study suggest that SCI plays a crucial role in improving LARGS supply chain practices and SSCP. The positive and significant impact of SCI on LARGS and SSCP is in line with previous studies [33,66,67]. It is evident that companies that prioritize SCI by introducing innovative products/services, focusing on new product development as a key source of competitive advantage, investing in research and development, and continually striving to enhance their products/processes experience improved LARGS supply chain and SSCP outcomes. This underscores the importance of embracing innovation as a key strategy for enhancing supply chain performance. It is essential to recognize that, in today’s business landscape, suppliers and customers are no longer viewed as competitors, but rather as integral members of a supply chain network. The collective goal of these stakeholders is to maximize profits and enhance productivity through a collaborative and mutually beneficial approach (win–win policy). The success of supply chain operations hinges on seamless collaboration and communication among suppliers, buyers, distributors, retailers, and customers throughout the production process. In conclusion, this study highlights the critical role of SCI in driving improvements in LARGS supply chain practices and SSCP [68]. Companies that actively embrace innovation within their supply chain operations are better positioned to achieve sustainable success and competitive advantages in the increasingly complex global marketplace.
Another finding indicates that adopting a LARGS supply chain approach has a positive and significant impact on SSCP. This aligns with previous studies [22,23,50,69,70], which have also highlighted the importance of strategic SCM practices in enhancing sustainability outcomes. By efficiently managing production factors to maximize efficiency, minimize costs, and deliver superior results while utilizing resources effectively, organizations can improve their SSCP. This includes responding promptly to market shifts, prioritizing adaptability in the face of uncertainty, and preparing for unexpected events and disruptions in the business environment. Moreover, producing environmentally friendly products and minimizing the environmental impact of operations not only meets customer needs and economic benchmarks, but also reduces material costs, energy consumption, and waste emissions. By incorporating these sustainable practices into SCM, organizations can enhance their sustainability performance while also achieving cost savings and meeting customer expectations. These findings underscore the importance of integrating sustainable practices into SCM for long-term success and competitiveness in the market.

6. Managerial Implication

Managers of large- and medium-sized manufacturing companies are advised to consider implementing blockchain technology (BT) within their supply chain operations. The utilization of BT enhances real-time product tracking and results in a substantial reduction in overall supply chain transportation costs. As a result, the integration of blockchain-driven supply chain analytics offers numerous benefits for improving supply chain sustainability. This technology holds the potential to address significant challenges related to tracking and monitoring throughout the supply chain, thereby enhancing operational efficiency across various aspects such as goods flow, information management for raw material storage and transportation, and the delivery of finished products. Ultimately, this fosters enhanced collaboration, simplified inventory control, and improved asset utilization.
Considering the role of supplier innovation, managers of large- and medium-sized manufacturing companies are suggested to encourage suppliers to consider innovative products/services, emphasize the presentation of new products/services as the main sources of competitive advantages, while the main suppliers of the company should be encouraged to introduce new products/services during meetings, the main suppliers should have a research and development department, and suppliers should introduce new products/processes to the company.
Considering the role of LARGS, the managers of large- and medium-sized manufacturing companies are suggested to consider the LARGS aspects of the supply chain when choosing a supplier. Opting for top-tier suppliers, with the aim of consolidating supplier numbers, has the potential to provide manufacturers with a competitive edge by lowering costs, enhancing product quality, and advancing product service development. In this context, the LARGS paradigm can exert a meaningful influence.

7. Conclusions

In summary, our findings suggest that the sustainability of SSCP can be impacted by blockchain-driven supply chain analytics, the LARGS supply chain, and SCI. We utilized a comprehensive approach, integrating SEM-ANFIS, to assess the proposed conceptual model. It was observed that both the LARGS supply chain and SCI act as significant and positive mediators in the influence of blockchain-driven supply chain analytics on SSCP. Moreover, the LARGS supply chain demonstrates a notable and positive mediating role in the impact of SCI on SSCP. Therefore, it can be inferred that the adoption of blockchain-driven supply chain analytics leads to an improvement in SSCP through the involvement of the LARGS supply chain and advancements in SCI. Consequently, companies that fail to embrace BT within their supply chain processes miss out on a critical opportunity to enhance the sustainability and overall performance of their supply chain. This is because blockchain-driven supply chain analytics not only fosters innovation and improves SSCP, but also expedites transactions, enhances security, and reduces costs within the supply chain process.
This research exclusively involved a sample of supply chain managers and vice presidents from large- and medium-sized manufacturing companies listed on the TSE, thus limiting the generalizability of the findings. Moreover, the findings relied on self-reported data. To address these limitations, future research could consider expanding the participant demographics to include a more diverse range of companies and geographical locations. Employing qualitative methods could provide deeper insights into the subjective experiences and perceptions of stakeholders involved in blockchain-driven supply chain analytics. Additionally, mixed-methods approaches could offer a comprehensive understanding by combining quantitative data with qualitative insights. Furthermore, utilizing other soft computing methods such as artificial intelligence, neural networks, or fuzzy logic [71,72,73,74,75,76,77,78] could enhance the analysis of complex relationships within supply chain operations affected by blockchain technology. These methods could provide more nuanced interpretations and predictive capabilities beyond traditional statistical approaches. Given the correlational nature of this study, caution should be exercised in inferring causation from the results. Future research could employ longitudinal designs or experimental approaches to establish causal relationships between the implementation of blockchain-driven supply chain analytics and various performance outcomes in manufacturing firms.

Author Contributions

Conceptualization, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); methodology, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); software, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); validation, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); formal analysis, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); investigation, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); writing—original draft preparation, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); writing—review and editing, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); visualization, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); supervision, S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć); project administration S.E., A.T., M.M., M.Z., G.G.A., M.D. (Maria Dzikuć) and M.D. (Maciej Dzikuć). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Centre, Poland, under the OPUS program, grant No. 2021/43/B/HS4/00422 Economic and social conditions for the development of renewable energy sources in rural areas in Poland.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request.

Conflicts of Interest

The authors declare no competing interests.

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Sustainability 16 06469 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. The tested model (** p < 0.01).
Figure 2. The tested model (** p < 0.01).
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Figure 3. The importance of determinants of SSCP.
Figure 3. The importance of determinants of SSCP.
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Figure 4. The relationships between factors and SSCP.
Figure 4. The relationships between factors and SSCP.
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Table 1. Measurement model reliability and validity results.
Table 1. Measurement model reliability and validity results.
VariableItemFactorCronbach’s AlphaCRAVE
Blockchain-driven supply chain analytics10.7390.9030.9200.562
20.787
30.736
40.724
50.768
60.728
70.775
80.755
90.729
SCI10.7970.8990.92110.662
20.861
30.853
40.830
50.772
60.765
Leanness10.8560.7870.8760.703
20.888
3767
Agility10.7000.6490.8110.589
20.805
30.794
Resilience10.7860.8110.8890.728
20.867
30.902
Greenness10.900.7730.8980.815
20.905
Sustainability10.8590.8640.9170.7878
20.865
30.934
SSCP10.8020.9060.9220.598
20.799
30.756
40.729
50.761
60.759
70.794
80.784
Table 2. The discriminant validity results.
Table 2. The discriminant validity results.
VariableBlockchain-Driven Supply Chain AnalyticsSCILARGSSSCP
Blockchain-driven supply chain analytics0.75
SCI0.44 **0.81
LARGS0.73 **0.57 **0.83
SSCP0.63 **0.56 **0.66 **0.77
** p < 0.01.
Table 3. Path coefficients and explained variance.
Table 3. Path coefficients and explained variance.
Variableβt-Valuep-ValueExplained Variance
On SSCP via: 0.594
Blockchain-driven supply chain analytics 0.285 **4.1190.01
SCI0.213 **3.7980.001
LARGS0.384 **4.8850.001
On LARGS via: 0.634
Blockchain-driven supply chain analytics0.593 **13.2690.001
SCI0.320 **6.9450.001
On SCI via:
Blockchain-driven supply chain analytics0.475 **0.87970.0010.226
** p < 0.01.
Table 4. Indirect coefficients.
Table 4. Indirect coefficients.
Indirect PathsIndirect Effectst-Valuep-Value
Blockchain-driven supply chain analytics → SC Innovation → LARGS SCM0.1524.9830.000
Blockchain-driven supply chain analytics → LARGS SCM → Sustainable SC Performance0.2284.2240.000
SC Innovation → LARGS SCM → SSCP0.1233.9230.000
Blockchain-driven supply chain analytics → SC Innovation → SSCP0.1013.0330.003
Table 5. Results of hypothesis testing.
Table 5. Results of hypothesis testing.
HypothesisResult
H1. LARGS supply chain practices are effective for SSCP.Confirmed
H2. SCI is effective for SSCP.Confirmed
H3. SCI is effective for LARGS SCM.Confirmed
H4. LARGS supply chain practices mediate the effect of SC innovation on SSCP.Confirmed
H5. Blockchain-driven supply chain analytics is effective for SSCP.Confirmed
H6. Blockchain-driven supply chain analytics is effective for SCI.Confirmed
H7. Blockchain-driven supply chain analytics is effective for LARGS supply chain practices.Confirmed
H8. SCI mediates the effect of blockchain-driven supply chain analytics on SSCPConfirmed
H9. LARGS supply chain practices mediate the effect of blockchain-driven supply chain analytics on SSCP.Confirmed
H10. SCI mediates the effect of blockchain-driven supply chain analytics on LARGS SCMConfirmed
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Espahbod, S.; Tashakkori, A.; Mohsenibeigzadeh, M.; Zarei, M.; Arani, G.G.; Dzikuć, M.; Dzikuć, M. Blockchain-Driven Supply Chain Analytics and Sustainable Performance: Analysis Using PLS-SEM and ANFIS. Sustainability 2024, 16, 6469. https://doi.org/10.3390/su16156469

AMA Style

Espahbod S, Tashakkori A, Mohsenibeigzadeh M, Zarei M, Arani GG, Dzikuć M, Dzikuć M. Blockchain-Driven Supply Chain Analytics and Sustainable Performance: Analysis Using PLS-SEM and ANFIS. Sustainability. 2024; 16(15):6469. https://doi.org/10.3390/su16156469

Chicago/Turabian Style

Espahbod, Shervin, Arash Tashakkori, Mahsa Mohsenibeigzadeh, Mehrnaz Zarei, Ghasem Golshan Arani, Maria Dzikuć, and Maciej Dzikuć. 2024. "Blockchain-Driven Supply Chain Analytics and Sustainable Performance: Analysis Using PLS-SEM and ANFIS" Sustainability 16, no. 15: 6469. https://doi.org/10.3390/su16156469

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