SCI is a broad research topic in supply chain management, aiming to increase value for end customers. SCI has three dimensions: "process integration," "supplier integration," and "customer integration" [23]. Customer integration refers to the extent to which a company collaborates with its customers to envision the future and engage in mutual planning [24]. Customer integration involves the two-way flow of information, services, and materials between the central company and customers. Customer integration helps organizations develop accurate demand forecasts, adapt to demand fluctuations, and implement postponement strategies [25]. Supplier integration refers to the extent to which a company partners with its suppliers to manage its inter-organizational processes, strategies, and behaviors in a coordinated and manageable manner to meet customer needs with minimal costs and rapid delivery [25]. Supplier integration, through knowledge sharing, reduces uncertainty [26]. Process integration provides easy access to a unified company database and connects all functional units of the company through a highly integrated information system [27]. Process integration enables all departments to have access to useful information for tracking and retrieving inventory status across the supply chain [28]. The three dimensions of SCI are further classified into two primary components: external integration and internal integration [25], which are used in the proposed model in this study. Customer value is provided through effective and efficient flows of financial resources, products, and information through inter- and intra-organizational collaboration in strategic, tactical, and operational dimensions of business processes [28]. SCI is achieved by involving upstream and downstream stakeholders and digitally linking them to intra- and inter-organizational business processes [23]. The role of SCI, based on existing literature, is a significant variable that affects supply chain performance [25].

Researchers consider blockchain as an innovation that combines information and communication technology with SCI, incorporating various other information and communication technologies such as software development tools, encryption technology, database technology, and data analytics [29]. In a supply chain, blockchain provides a high level of integration and serves as a unified source of information for all supply chain partners [30]. Blockchain supports all areas of the supply chain, including planning, procurement, manufacturing, and delivery, ultimately enabling transparency in transactions [31]. Manufacturing industries, supply chain management, retail, and leasing are some key functional areas of the automotive supply chain that can leverage blockchain technology [32].

Additionally, blockchain can be used for identifying and reporting counterfeit parts, making product recalls more cost-effective, and facilitating faster and more secure transactions by eliminating intermediaries [33]. The new wave of digital transformation has compelled automotive companies to innovate and embrace new technologies. Automotive companies need to shift their focus from optimizing sales prices to optimizing the cost of the product lifecycle, creating competitive advantage, and unlocking new business opportunities [11].

"Capability is considered dynamic when it enhances an organization's capacity for decision-making, problem-solving, identifying opportunities and threats, and reallocating existing resources" [34]. Perks et al. [35] argue that blockchain brings together all supply chain partners to create value by fulfilling assigned tasks that are achieved through the dynamic configuration of supply chain resources. Dynamic capability is defined as the "ability of an organization to purposefully create, extend, or modify its resource base" [36]. According to the theory of dynamic capabilities, organizations can create "value" by improving supply chain processes and resources. The primary value drivers of blockchain include increased transparency, immutability, audibility of transactions, reliability, and trustworthiness [11]. It is expected that these value drivers will reduce transaction-based costs, add new services, delineate organizational boundaries, and automate decision-making in the supply chain [37].

Sustainable supply chain performance plays a crucial role in the overall success of an organization. Timely and efficient supply chain assessment contributes to the development, execution, and monitoring of strategies [38]. As supply chains extend in scope and activities, event tracing becomes a significant challenge. Customers and buyers will perceive real value in products or services when supply chains provide transparency and present details of various historical events related to the product or service. Transforming the supply chain into a digital ecosystem requires the opening up of existing interdependencies among supply chain participants and access to information for all supply chain participants. All supply chain participants are crucial for the successful integration of collective value creation and shared responsibility [11]. Many previous studies dealing with sustainable supply chains have highlighted the inclusion of economic, environmental, and social dimensions of sustainability [39].

The current research focuses on economic sustainability instead of short-term goals such as profitability improvement and efficiency. It emphasizes the ability of organizations in the automotive supply chain to achieve long-term financial performance. Thus, this research defines the economic sustainability of the supply chain as a driver of improving the financial performance of both the organization and the supply chain through systematic and strategic alignment of multiple business functions [40].

The involvement of multiple actors in a supply chain network leads to a lack of transparency and accountability [41]. Therefore, as digitalization has advanced in the supply chain field, blockchain improves secure transactions, enhances transparency, and enables product traceability [42], resulting in cost reduction and improved performance of sustainable supply chains [43]. Other benefits of blockchain include increased accountability and auditing [42], prevention of fraud [22], privacy, cybersecurity, and protection [42], as well as improved financial processes [44]. The trust level provided by active blockchain in the supply chain is a fundamental element in developing supply chain relationships and improving sustainable supply chain performance [45].

Blockchain digitally integrates multiple stakeholders and facilitates product tracking and transaction settlement through process automation and smart contract execution [46]. The SCI provided by blockchain is highly secure and prevents unauthorized access to stored information [47]. Blockchain enables exceptional levels of SCI, harmonizing customer, supplier, and manufacturer information [11]. Kshetri [42] suggests integrating blockchain with the Internet of Things to identify the source of supply chain disruptions, which can successfully address crises during product recalls. Similarly, integrating blockchain with other emerging technologies reduces uncertainty, and increases supply chain transparency [14], process integration [48], and traceability [49]. Blockchain enhances the privacy, auditability, and operational efficiency of supply chains [50]. Literature indicates that SCI affects sustainable supply chain performance [19]. Recent studies analyze the impact of integration technologies based on communication technologies such as the Internet of Things [51] and supply chain information systems [52] on sustainable supply chain performance.

Aghajani Mir et al. [64] conducted research titled 'identification and prioritization of challenges in implementing blockchain technology in supply chain using group bwm approach'. They found that security, technical, and organizational challenges are the most important challenges faced by companies in implementing this technology. Among all the sub-indicators of the research challenges, scalability, privacy/confidentiality, and cyber-attacks were found to be of the highest importance. Sharaj Sharifi and Mirfalah [65] conducted a research titled 'the impact of blockchain on SCI with the mediating role of information technology capability'. They concluded that blockchain has a positive impact on SCI. Therefore, this study conceptualizes blockchain as a dynamic capability by accepting the theory of dynamic capabilities. It examines its direct impact on internal and external SCI and its indirect impact on sustainable supply chain performance. Embracing blockchain technology in a manufacturing company is essential for synchronizing the flow of products and information within and across the supply chain. Hence, the findings of this study can assist policymakers and supply chain professionals in developing relevant policies for the automotive industry, considering customer responsiveness.

Finally, the conceptual model of the research is presented in Fig. 1, which is an extension of the model developed by Balhaddi et al. [67]. In this model, dashed lines represent the research paths of Balhaddi et al. [67], and the solid line represents the added variable chain in the model".

Based on the provided information, the main objective of this research is to investigate the indirect impact of customer responsiveness on the economic sustainability of the supply chain by accepting blockchain technology as a dynamic capability in the automotive industry. The sub-objectives are as follows:

I. H 1: Accepting blockchain technology has an impact on the economic sustainability of the supply chain. II. H 2: SCI has an impact on customer responsiveness in the supply chain.

III. H 3: Customer responsiveness has an indirect effect on the economic sustainability of the supply chain. IV. H 4: SCI has an indirect effect on the economic sustainability of the supply chain by accepting blockchain technology.

In the framework of structural equation modeling, the relationships between measured and latent variables are considered. The Bayesian approach, using Markov Chain Monte Carlo Methods (MCMC) to estimate parameters based on predefined prior distributions for unobserved variables, has distinct advantages over classical SEM approaches [70]. In maximum likelihood estimation, the true values of model parameters are treated as fixed but unknown, while parameter estimation is based on a specific observed data sample. In contrast, Bayesian estimation treats any unknown quantity as a random variable and assigns a probability distribution to it; thus, from a Bayesian perspective, the true parameters of the model are uncertain and treated as random. These parameters are then assigned a joint distribution, where the prior distribution (the probability distribution of these parameters before they are observed) is combined with the posterior distribution (the probability distribution of the parameters after they are observed and combined with the prior distribution) based on a well-known formula called Bayes' theorem, reflecting initial beliefs about parameter estimates [71]. Two characteristics of this joint distribution are important for confirmatory factor analyses. First, the mean of this posterior distribution can be reported as the parameter estimate. Second, the standard derivative of the posterior distribution is used as a reference for standard error in maximum likelihood estimation [69]. AMOS generates a default sample size of 505 cases, which is stored in memory before the first sample is designed for analysis. The reason for these default samples is to allow the Markov chain Monte Carlo algorithm to converge with the true joint posterior distribution [71]. The convergence statistic value is 0.001, which is smaller than the value of 0.0021 recommended by Gelman et al. [72], indicating convergence [69]. The results of the BSEM are presented in Fig. 2.

After the simulation is stopped, we examine the evaluation indices of model fit. The most important indicator for fitting Bayesian structural equations is the Posterior Predictive P-value (PPP) Fig. 3, which has a value of 0.000 in the fitted model and is acceptable at a 1% level of error [72].

Additionally, the model fit indices based on the traditional ML approach are summarized in Table 4, which indicates that the results support the validity of the model.

In Fig. 2, the path coefficients between the variables in the model, corresponding to the research hypotheses, are observable. These standardized path coefficients are numeric values between 1 and -1, where the positive sign indicates a positive relationship between the research variables. For example, the path coefficient between blockchain technology acceptance and the economic sustainability of the supply chain is 0.37. However, is this coefficient statistically significant? To answer this question, we use the corresponding t-statistic (T) value associated with each coefficient, which can be found in Table 5.

Based on Fig. 2, the results of the structural model evaluation are reported in the table below:

These results suggest that the path coefficients for Paths 1, 3, and 5 are statistically significant, while the coefficients for Paths 2 and 4 are not statistically.

The results of the indirect effect testing between variables using the mediation approach are shown in Table

In statistics, to confirm a hypothesis at a 95% confidence level, the t-value corresponding to that test should be greater than 1.96. As you can see in Table 5 and Table 6, the t-values for the paths that are greater than 1.96 indicate that these path coefficients are significant. Since there is a significant direct path between the independent variable, blockchain technology acceptance, and the dependent variable, economic sustainability of the supply chain, the mediating variables, SCI, and customer responsiveness are considered complete mediators.