Analyzing Barriers of BIM and Blockchain Integration: A Hybrid ISM-DEMATEL Approach

Abstract

Building information modeling (BIM) and blockchain are reshaping construction business processes. This is particularly important for efficient information management and collaboration, especially in the current environment of complexity and fragmentation in construction business processes. However, due to the limits of practical experience and exploration, construction organizations continue to face significant challenges in adopting integrated BIM and blockchain. This study concentrates on exploring the integration of BIM and blockchain by identifying and analyzing key barriers. Through a systematic literature review and expert consultation, 13 major barriers have been identified. Relationships among barriers have been established using interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) approaches. The analysis shows that high initial costs and legal and regulatory limits are the root causes that affect the adoption of BIM and blockchain integration. Additionally, investment and return risk, stakeholder attitudes and unclear value proposition have a great impact on the overall system. These findings can help construction practitioners in developing and planning strategies for the effective implementation of BIM and blockchain integration.

1. Introduction

Smart construction involves the organic integration of a new generation of information technologies with construction practices, and is a key driver of high-quality development in the construction industry [1]. However, despite the widespread promotion of smart construction, the traditional construction sector continues to face significant challenges. Notable issues such as insufficient trust among stakeholders, limited information sharing, complex business processes, and payment delays hinder the sustainable development of the industry [2]. As smart construction evolves rapidly, digital technologies like building information modeling (BIM) and blockchain have matured, and are being increasingly adopted. These technologies are reshaping production methods, business models, value chain distribution, and competitive dynamics in the construction industry. Moreover, they provide robust technical support for refined management and intensive development [3].

BIM and blockchain integration has received extensive attention from scholars [4]. BIM is a transformative technology that revolutionizes conventional practices [5], while blockchain, as an advanced and decentralized system, is widely recognized as a disruptive innovation under “Construction 4.0” [2]. Given their respective advantages, scholars have explored the potential of their integration. For instance, Elghaish et al. (2023) [6] highlighted that BIM and blockchain integration enables a digital circular construction supply chain. Celik et al. (2023) [7] found that BIM and blockchain integration enhances collaboration, efficiency, and traceability in construction. Hsu et al. (2023) [8] revealed that BIM and blockchain integration facilitates knowledge sharing in construction projects. Yoon et al. (2024) [9] demonstrated that BIM and blockchain integration improves the effectiveness and fairness of construction project procurement. Elsharkawi et al. (2025) [10] showed how the combination of smart contracts and BIM can automate construction payments.

Despite the numerous advantages of BIM and blockchain integration, several challenges persist [4]. Xue and Lu (2020) [11] emphasized that integrating BIM with blockchain is a formidable task due to the issue of information redundancy. Tao et al. (2022) [12] cautioned that directly integrating BIM with blockchain entails a risk of exposing sensitive data. Xu et al. (2024) [3] identified three key areas in the challenges of BIM and blockchain integration: technology, organization and environment. More importantly, although prior research has investigated BIM–blockchain integration, most studies have focused on technical aspects, with limited attention to integration management. Construction organizations continue to face significant barriers due to limited contextual knowledge, insufficient practical experience, and a lack of awareness of the complexities involved.

This study aims to identify barriers to BIM and blockchain integration in smart construction, providing guidance for practitioners and addressing the existing research gaps. Although a few studies have employed multi-criteria decision-making (MCDM) strategies or empirical methods to evaluate interrelationships among integration barriers, such approaches remain limited. Most existing studies focus on qualitative analysis or system design rather than exploring dynamic interactions between integration barriers. Based on the above analysis, the study formulates the following research objectives:

(a)

Identify the barriers to BIM and blockchain integration;

(b)

Determine the contextual relationships among the identified barriers;

(c)

Assess the causal relationships between barriers.

To achieve these objectives, a hybrid research method was adopted. First, barriers to BIM and blockchain integration were identified through a systematic literature review and validated through expert consultation. Next, the Interpretive Structural Model (ISM) method was used to analyze the contextual relationships among these barriers. To further assess the strength and causal relationships among the barriers, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was applied. This study contributes to the understanding of BIM and blockchain integration by identifying barriers across technical, organizational, and institutional levels, thereby providing deeper insights into the challenges of digital transformation in the construction industry. Additionally, it provides targeted strategies for policymakers and industry practitioners to facilitate the effective integration of BIM and blockchain.

2. Literature Review

2.1. Integration of BIM and Blockchain

BIM and blockchain are reshaping the future of the construction industry. From a narrow technical perspective, BIM is typically defined as a data-rich, object-oriented, 3D digital representation of the physical and functional characteristics of a construction project [13]. It serves as a shared knowledge resource for information throughout a building’s lifecycle [1]. From a broader perspective, BIM is increasingly being conceptualized as an integrated process or methodology that facilitates collaboration among stakeholders across all phases of a building’s lifecycle [14]. Obviously, BIM is not merely a technology, but also a project management tool and process. It enhances design accuracy, streamlines construction processes, and supports effective project operation and maintenance [3]. In addition, as the foundation of digital twins, BIM also enables integration with emerging technologies such as blockchain [15]. Blockchain, as a distributed ledger technology, ensures data security and transparency through decentralized networks and encryption algorithms [2], enabling the immutability and traceability of information [16]. Given these advantages, scholars have increasingly explored the integration of BIM and blockchain. Existing research on BIM and blockchain integration primarily focuses on two key areas.

The first area emphasizes the advantages of BIM and blockchain integration from a qualitative perspective. For instance, Nawari and Ravindran (2019) [17,18] surveyed the application of blockchain in the construction industry and analyzed its potential advantages in the BIM process. Mohammed et al. (2021) [19] examined how blockchain enhances construction workflows and advocated for its integration with BIM to optimize business processes. Hijazi et al. (2021) [20] reviewed BIM and blockchain integration in the construction supply chain, providing a rationale for their integration. Zhang et al. (2023) [21] suggested that through BIM and blockchain integration, construction organizations can avoid various problems and obtain multiple benefits. Elghaish et al. (2023) [6] pointed out that the integration of BIM and blockchain is a feasible strategy to realize a digital circular building supply chain. Celik et al. (2023) [7] indicated that the integration of BIM and blockchain can improve collaboration, efficiency and traceability in construction. Yu et al. (2024) [22] reviewed the current status and future opportunities of blockchain and BIM integration from a socio-technical perspective. Xu et al. (2024) [3] identified five key management impacts of BIM and blockchain integration through a hybrid study.

The second area explores the technical frameworks and system designs of BIM and blockchain integration. For instance, Tao et al. (2022) [12] outlined a blockchain-based confidentiality framework for BIM design collaboration to protect sensitive BIM data. Li et al. (2022) [23] introduced an innovative service-oriented system architecture for a blockchain-based IoT–BIM platform designed to support data–information–knowledge-driven supply chain management. Celik et al. (2023) [24] developed a blockchain-based BIM data provenance model to facilitate information exchange in construction projects. Brandín and Abrishami (2024) [25] proposed an integrated framework combining IoT, BIM, and blockchain to optimize efficiency and transparency in the construction supply chains. Yoon et al. (2024) [9] presented a proof-of-concept for a smart contract-enabled BIM procurement system in construction management. Elsharkawi et al. (2025) [10] employed an advanced technique to automate construction payments through the integration of smart contracts and scanning into BIM.

Based on the above literature review, it can be seen that scholars have explored the integration of BIM and blockchain to a certain extent. However, except for some qualitative studies, most studies are based on the perspective of technical integration framework development, and few studies have explored it from the perspective of integrated management, especially in the determination and interaction of barrier factors. Therefore, it is necessary for future research to explore the barriers to the integration of BIM and blockchain from the dual perspectives of technology and management, strengthen stakeholders’ understanding of the integration of the two, and further promote the effective application of the integration of the two.

2.2. Barriers to BIM and Blockchain Integration

Although BIM and blockchain integration has the potential to bring significant benefits and gains to the construction industry, its practical adoption continues to face numerous barriers and challenges. Identifying and understanding these barriers is critical for facilitating their effective adoption and implementation. This section systematically identifies and categorizes the barriers to BIM and blockchain integration through a comprehensive review of existing literature, industry reports, and expert feedback.

A systematic literature review was conducted to sort out the barriers preventing the integration of BIM and blockchain. This method involves the rigorous, transparent, and replicable collection, evaluation, analysis, and synthesis of existing research to address a specific research question [3]. This study selected Scopus and Web of Science for retrieval. The initial search was conducted on 1 January 2025, and updated on 1 March 2025. The search strategy used the following keyword combination: (“Blockchain”) and (“BIM” or “building information modeling” or “Building information modelling”). To ensure the quality and reliability of the search results, several screening criteria were set, including that only peer-reviewed journal articles published in English be retained. The initial search yielded 350 results from Scopus and 283 from Web of Science. After removing duplicates, screening titles and abstracts, and conducting full-text reviews, 60 relevant studies were retained. In addition, two more articles were identified through extended reading, resulting in a final selection of 62 documents used to identify and categorize barriers to BIM and blockchain integration. The detailed literature retrieval and screening process is shown in Figure 1.

In addition to academic literature, industry reports from various websites and consulting agencies (such as Arup and the Institution of Civil Engineers) were retrieved to gain further insights into the topic. Finally, based on the discussions and feedback from industry experts and scholars, 13 key barriers to BIM and blockchain integration were identified. The identified barriers, along with their main references, are shown in

Table 1. Barriers to BIM and blockchain integration.

Code	Barriers	Main References
IB1	Infrastructure and technology immaturity	[3,22,26,27]
IB2	Complexity of technology integration	[4,6,9,10,27]
IB3	Data privacy and security	[3,4,21,27,28,29]
IB4	Lack of standards and protocols	[4,23,30,31,32]
IB5	Lack of expertise and experience	[4,10,11,22,25,33,34]
IB6	Lack of training and education	[10,17,18,35]
IB7	High initial cost	[3,4,10,17,18,33]
IB8	Investment and return risk	[4,17,18,36]
IB9	Unclear value proposition	[26,31,35,37]
IB10	Stakeholder attitudes	[27,32,36,38]
IB11	Lack of trust and collaboration	[4,7,25,36,39,40]
IB12	Workflow changes and adjustments	[10,28,30,32]
IB13	Legal and regulatory	[3,10,17,18,33,41]

.

3. Methods

This study combined ISM and DEMATEL to explore the interrelationships among barriers to BIM and blockchain integration. These two methods are widely used for complex problem analysis and decision support, and they are considered complementary. ISM is mainly employed to examine the relationships among elements in complex systems and to analyze the system’s structure and the hierarchical relationships among different factors [42]. Its specific steps are to first identify the key elements in the system, then determine their interrelationships, after which constructing a structural model through matrix operations, including a structural self-interaction matrix (SSIM), an initial reachability matrix (IRM), and a final reachability matrix (FRM) [43,44]. DEMATEL is a tool grounded in graph theory and matrix, which is mainly utilized to address the cause–effect relationships in complex problems [45]. Its specific steps are as follows: first, collect experts’ evaluations of the relationships between various factors, then construct an average direct influence matrix, obtain the total relation matrix (TRM) through matrix operations, and finally generate a causal diagram and analyze the weight and impact direction of each factor [43,45].

Both ISM and DEMATEL perform well in studies with small sample sizes [44]. ISM offers significant benefits as it presents results through a hierarchical topology diagram. This display is very intuitive. Through the hierarchical topology diagram, the causal relationships and hierarchical structure between system factors can be clearly understood at a glance. However, one limitation of ISM is that it does not quantify the strength of these relationships—an important consideration given that not all influences are equal in practice. DEMATEL compensates for this limitation by quantifying the strength of influence between variables, thereby offering deeper insights into the causal dynamics among factors [45]. In addition, the integration of ISM and DEMATEL enhances methodological robustness. By using the output of ISM as an input for DEMATEL, the combined approach reduces the computational burden on experts, simplifies mathematical operations, and further improves the reliability and interpretability of the results [44,46]. Figure 2 shows the specific ISM-DEMATEL method flow.

4. Analysis and Results

4.1. Data Collection

To ensure the quality and depth of expert input, participants were recruited from conferences and forums related to smart construction and blockchain technology. Such environments encourage open knowledge exchange, and importantly, attract professionals from a wide range of organizations. This diversity helped enhance the reliability and representativeness of the data collected. A total of 36 experts with relevant domain experience were contacted, and 15 agreed to participate in the study. These participants were all from leading construction firms, engineering consulting agencies, and renowned universities, and had eight years of experience in areas related to BIM, blockchain, or construction management. The specific data collection involves two stages. In the initial stage, experts were asked to assess the contextual relationships between each pair of influencing factors using the symbols “V”, “A”, “X”, and “O”, thereby generating an SSIM. In the second stage, based on the ISM model output, a follow-up survey was conducted in which the same experts were invited to quantify the strength of relationships between significantly related variables. This was done using a five-point scale ranging from 0 (no influence) to 4 (very high influence), following the DEMATEL methodology.

4.2. ISM Analysis

4.2.1. Constructing the SSIM

In development of the SSIM, the symbols “V”, “A”, “X”, and “O” were used to represent the contextual relationships between pairs of barriers [47]. Specifically, “V” indicates that the horizontal (row) barrier influences the vertical (column) barrier; “A” indicates that the vertical barrier influences the horizontal barrier; “X” denotes mutual influence between the two barriers; and “O” signifies no relationship between them [48]. The data results are shown in

Table 2. The structural self-interaction matrix result.

Code	1	2	3	4	5	6	7	8	9	10	11	12	13
IB1	—	V	V	O	A	A	A	V	O	V	O	O	O
IB2		—	A	A	A	O	O	V	O	V	V	A	A
IB3			—	O	A	A	V	V	O	V	V	O	O
IB4				—	X	X	O	V	O	V	V	V	A
IB5					—	V	V	V	V	X	V	V	A
IB6						—	A	X	X	A	A	A	A
IB7							—	V	V	V	O	A	V
IB8								—	X	V	A	X	A
IB9									—	X	X	X	A
IB10										—	X	V	A
IB11											—	X	A
IB12												—	A
IB13													—

.

4.2.2. Constructing the IRM and FRM

The above SSIM structural relationships were converted into IRM. The symbols “V”, “A”, “X”, and “O” can be substituted according to the 0 or 1 transformation principle. The IRM is shown in

Table 3. The initial reachability matrix result.

Code	1	2	3	4	5	6	7	8	9	10	11	12	13
IB1	1	1	1	1	0	0	0	1	0	1	1	0	0
IB2	1	1	0	0	0	0	0	1	0	1	1	1	0
IB3	1	1	1	1	0	0	0	1	0	1	1	0	0
IB4	1	1	1	1	0	0	0	1	0	1	1	1	0
IB5	1	1	1	1	1	1	0	1	1	1	1	1	0
IB6	1	0	1	1	1	1	0	1	1	1	1	1	0
IB7	1	0	1	0	1	1	1	1	1	1	0	1	0
IB8	0	0	0	0	0	0	0	1	1	1	0	0	0
IB9	0	0	0	0	0	0	0	1	1	1	0	0	0
IB10	0	0	0	0	0	0	0	1	1	1	0	0	0
IB11	0	0	0	0	0	0	0	1	1	1	1	1	0
IB12	0	0	0	0	0	0	0	1	1	1	1	1	0
IB13	0	1	0	1	1	1	0	1	1	1	1	1	1

.

Based on the IRM, the FRM was calculated. A key step in this process was checking for transitivity within the IRM [44,49]. Transitivity is a fundamental assumption in the ISM methodology; that is, if IB1 has an impact on IB2, and IB2 has an impact on IB3, then it is assumed that IB1 also has an impact on IB3. The reachability matrix indicates whether a linkage path exists from one influencing factor to another [50]. After checking the transitivity of the IRM, the 0 in the cells with transitivity was replaced with 1*, and then the FRM was constructed. The FRM is presented in

Table 4. The final reachability matrix result.

Code	1	2	3	4	5	6	7	8	9	10	11	12	13	Driving Power
IB1	1	1	1	1	0	0	0	1	1*	1	1	1*	0	9
IB2	1	1	1*	1*	0	0	0	1	1*	1	1	1	0	9
IB3	1	1	1	1	0	0	0	1	1*	1	1	1*	0	9
IB4	1	1	1	1	0	0	0	1	1*	1	1	1	0	9
IB5	1	1	1	1	1	1	0	1	1	1	1	1	0	11
IB6	1	1*	1	1	1	1	0	1	1	1	1	1	0	11
IB7	1	1*	1	1*	1	1	1	1	1	1	1*	1	0	12
IB8	0	0	0	0	0	0	0	1	1	1	0	0	0	3
IB9	0	0	0	0	0	0	0	1	1	1	0	0	0	3
IB10	0	0	0	0	0	0	0	1	1	1	0	0	0	3
IB11	0	0	0	0	0	0	0	1	1	1	1	1	0	5
IB12	0	0	0	0	0	0	0	1	1	1	1	1	0	5
IB13	1*	1	1*	1	1	1	0	1	1	1	1	1	1	12
Dependence power	8	8	8	8	4	4	1	13	13	13	10	10	1

.

4.2.3. Partitioning the Hierarchical Structure

To better understand the hierarchical relationships between the influencing factors in the ISM model, it was necessary to divide the hierarchical structure of each factor. The hierarchical division was mainly determined by the reachability matrix. Based on the reachability matrix results, the reachable set, predecessor set and common set for each barrier could be expressed. The reachable set for a given barrier consisted of the elements with a value of 1 in the corresponding row of the reachability matrix. For example, the reachable set of IB11 was (8, 9, 10, 11, 12). The predecessor set for a given barrier included the elements with a value of 1 in the corresponding column. For instance, the predecessor set of IB5 was (5, 6, 7, 13). The common set for a given barrier was the intersection of its reachable and predecessor sets. For example, the common set of IB6 was (5, 6). This study adopted a result-first hierarchical extraction rule method. The specific steps were to compare the reachable set of each barrier with its common set. If the reachable set was identical to the common set, the barrier was assigned to the highest level in the system [44]. Similarly, the barriers obtained in the first iteration were eliminated, and the values corresponding to those barriers were eliminated from other sets. This comparison process continued iteratively until the hierarchical relationships of all barriers were determined. The detailed iterative process and hierarchical division results are presented in

Table 5. Partitioning the hierarchical structure.

Interaction	Code	Reachability Set	Antecedent Set	Interaction Set	Level
First	IB1	1, 2, 3, 4, 8, 9, 10, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB2	1, 2, 3, 4, 8, 9, 10, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB3	1, 2, 3, 4, 8, 9, 10, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB4	1, 2, 3, 4, 8, 9, 10, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB5	1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12	5, 6, 7, 13	5, 6
	IB6	1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12	5, 6, 7, 13	5, 6
	IB7	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12	7	7
	IB8	8, 9, 10	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13	8, 9, 10	I
	IB9	8, 9, 10	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13	8, 9, 10	I
	IB10	8, 9, 10	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13	8, 9, 10	I
	IB11	8, 9, 10, 11, 12	1, 2, 3, 4, 5, 6, 7, 11, 12, 13	11, 12
	IB12	8, 9, 10, 11, 12	1, 2, 3, 4, 5, 6, 7, 11, 12, 13	11, 12
	IB13	1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13	13	13
Second	IB1	1, 2, 3, 4, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB2	1, 2, 3, 4, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB3	1, 2, 3, 4, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB4	1, 2, 3, 4, 11, 12	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4
	IB5	1, 2, 3, 4, 5, 6, 11, 12	5, 6, 7, 13	5, 6
	IB6	1, 2, 3, 4, 5, 6, 11, 12	5, 6, 7, 13	5, 6
	IB7	1, 2, 3, 4, 5, 6, 7, 11, 12	7	7
	IB11	11, 12	1, 2, 3, 4, 5, 6, 7, 11, 12, 13	11, 12	II
	IB12	11, 12	1, 2, 3, 4, 5, 6, 7, 11, 12, 13	11, 12	II
	IB13	1, 2, 3, 4, 5, 6, 11, 12, 13	13	13
Third	IB1	1, 2, 3, 4	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4	III
	IB2	1, 2, 3, 4	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4	III
	IB3	1, 2, 3, 4	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4	III
	IB4	1, 2, 3, 4	1, 2, 3, 4, 5, 6, 7, 13	1, 2, 3, 4	III
	IB5	1, 2, 3, 4, 5, 6	5, 6, 7, 13	5, 6
	IB6	1, 2, 3, 4, 5, 6	5, 6, 7, 13	5, 6
	IB7	1, 2, 3, 4, 5, 6, 7	7	7
	IB13	1, 2, 3, 4, 5, 6, 13	13	13
Fourth	IB5	5, 6	5, 6, 7, 13	5, 6	IV
	IB6	5, 6	5, 6, 7, 13	5, 6	IV
	IB7	5, 6, 7	7	7
	IB13	5, 6, 13	13	13
Fifth	IB7	7	7	7	V
	IB13	13	13	13	V

.

4.2.4. ISM Model

The ISM model diagram of the barriers was drawn based on the reachability matrix and the results of the hierarchical relationship division. After thorough inspection, no internal inconsistencies were found in the model. The 13 barriers were categorized into five levels, with the final ISM model diagram presented in Figure 3. As illustrated in the figure, IB7 (High initial cost) and IB 13 (Legal and regulatory) are positioned at the bottom, and IB8 (Investment and return risk), IB9 (Unclear value proposition), and IB10 (Stakeholder attitudes) are placed at the top.

4.2.5. MICMAC Analysis

The MICMAC analysis was based on the principle of matrix multiplication, aiming to determine the “driving forces and dependent forces” of each barrier, and then classify the research variables accordingly [46]. Using the reachability matrix, the driving forces and dependencies of all barriers were calculated, with the results displayed in Table 4. Based on these results, the 13 barriers in this study were divided into four quadrants: I—autonomous clusters, II—dependent clusters, III—linkage clusters and IV—driving clusters. Figure 4 presents the MIMAC analysis results, revealing that no barrier was classified in the I—autonomous cluster. This indicates that all selected barriers were sufficient to ensure valid outcomes [44]; the II—dependent cluster contained IB8, IB9, IB10, IB11 and IB12, indicating that these factors were mainly affected by other factors; the III—linkage cluster contained IB1, IB2, IB3 and IB4, indicating that they were inherently unstable, and the IV—driving cluster contained IB5, IB6, IB7 and IB13, indicating that these factors mainly drove other barriers.

4.3. DEMATEL Analysis

4.3.1. Average Direct Influence Matrix

The output of the ISM method was used as the input of the DEMATEL method to construct the average direct impact matrix of the barriers. Specifically, only variables exhibiting an impact relationship in the ISM model were considered in the collection of data from experts. Experts were asked to assess the degree of influence between the corresponding barriers using a 0–4 scoring system, based on their impact on other barriers. The average direct impact matrix for the barriers to BIM and blockchain integration was then calculated by summarizing and averaging the data. The results are shown in

Table 6. Average direct influence matrix.

Code	1	2	3	4	5	6	7	8	9	10	11	12	13
IB1	0.000	3.467	3.067	3.200	0.000	0.000	0.000	1.067	1.000	1.189	2.333	2.533	0.000
IB2	3.733	0.000	2.800	2.867	0.000	0.000	0.000	1.133	0.933	1.133	2.267	2.000	0.000
IB3	3.200	3.133	0.000	3.333	0.000	0.000	0.000	1.000	0.667	1.533	2.200	2.333	0.000
IB4	2.933	3.000	3.200	0.000	0.000	0.000	0.000	0.959	1.067	1.333	2.200	2.133	0.000
IB5	2.333	2.333	2.267	2.200	0.000	3.667	0.000	1.333	1.467	1.400	1.133	1.067	0.000
IB6	2.467	2.400	2.067	2.133	3.600	0.000	0.000	1.400	1.600	1.533	1.267	1.333	0.000
IB7	2.000	2.133	1.533	2.200	3.333	3.533	0.000	1.133	1.067	1.267	1.333	1.533	0.000
IB8	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	3.600	3.667	0.000	0.000	0.000
IB9	0.000	0.000	0.000	0.000	0.000	0.000	0.000	3.467	0.000	3.533	0.000	0.000	0.000
IB10	0.000	0.000	0.000	0.000	0.000	0.000	0.000	3.333	3.533	0.000	0.000	0.000	0.000
IB11	0.000	0.000	0.000	0.000	0.000	0.000	0.000	2.667	2.467	3.000	0.000	3.067	0.000
IB12	0.000	0.000	0.000	0.000	0.000	0.000	0.000	3.067	3.133	2.933	3.200	0.000	0.000
IB13	1.867	2.000	2.533	2.267	3.533	3.333	0.000	1.000	1.200	1.133	1.467	1.400	0.000

.

4.3.2. Normalized Average Direct Influence Matrix

According to the average direct impact matrix, the elements in each row were summed, and the maximum sum value among the elements was identified. Each element was then divided by this maximum value, resulting in the normalized average direct impact matrix. The results are displayed in

Table 7. Normalized average direct influence matrix.

Code	1	2	3	4	5	6	7	8	9	10	11	12	13
IB1	0.000	0.160	0.141	0.147	0.000	0.000	0.000	0.049	0.046	0.055	0.107	0.117	0.000
IB2	0.172	0.000	0.129	0.132	0.000	0.000	0.000	0.052	0.043	0.052	0.104	0.092	0.000
IB3	0.147	0.144	0.000	0.153	0.000	0.000	0.000	0.046	0.031	0.071	0.101	0.107	0.000
IB4	0.135	0.138	0.147	0.000	0.000	0.000	0.000	0.044	0.049	0.061	0.101	0.098	0.000
IB5	0.107	0.107	0.104	0.101	0.000	0.169	0.000	0.061	0.067	0.064	0.052	0.049	0.000
IB6	0.113	0.110	0.095	0.098	0.166	0.000	0.000	0.064	0.074	0.071	0.058	0.061	0.000
IB7	0.092	0.098	0.071	0.101	0.153	0.163	0.000	0.052	0.049	0.058	0.061	0.071	0.000
IB8	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.166	0.169	0.000	0.000	0.000
IB9	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.160	0.000	0.163	0.000	0.000	0.000
IB10	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.153	0.163	0.000	0.000	0.000	0.000
IB11	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.123	0.113	0.138	0.000	0.141	0.000
IB12	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.141	0.144	0.135	0.147	0.000	0.000
IB13	0.086	0.092	0.117	0.104	0.163	0.153	0.000	0.046	0.055	0.052	0.067	0.064	0.000

.

4.3.3. Constructing the TRM

Then, the comprehensive impact matrix was calculated using the formula T = D(I − D)⁻¹, where I represent the unit matrix and D represents the standardized average direct impact matrix [51]. By calculating the inverse matrix of the I-D matrix and multiplying it with the matrix D, the TRM was finally obtained, as illustrated in

Table 8. The total relation matrix result.

Code	1	2	3	4	5	6	7	8	9	10	11	12	13	Row Total
IB1	0.104	0.239	0.220	0.228	0.000	0.000	0.000	0.216	0.211	0.235	0.222	0.228	0.000	1.904
IB2	0.248	0.099	0.208	0.213	0.000	0.000	0.000	0.210	0.200	0.224	0.214	0.204	0.000	1.820
IB3	0.232	0.227	0.096	0.232	0.000	0.000	0.000	0.210	0.196	0.242	0.215	0.219	0.000	1.869
IB4	0.217	0.217	0.220	0.094	0.000	0.000	0.000	0.203	0.204	0.230	0.209	0.206	0.000	1.802
IB5	0.231	0.228	0.218	0.219	0.029	0.174	0.000	0.245	0.250	0.263	0.183	0.180	0.000	2.220
IB6	0.234	0.229	0.210	0.215	0.170	0.029	0.000	0.253	0.260	0.273	0.189	0.190	0.000	2.253
IB7	0.238	0.240	0.208	0.238	0.185	0.194	0.000	0.261	0.260	0.283	0.210	0.217	0.000	2.534
IB8	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.067	0.212	0.214	0.000	0.000	0.000	0.492
IB9	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.202	0.067	0.208	0.000	0.000	0.000	0.477
IB10	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.196	0.206	0.067	0.000	0.000	0.000	0.469
IB11	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.214	0.210	0.231	0.021	0.144	0.000	0.820
IB12	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.238	0.242	0.238	0.150	0.021	0.000	0.890
IB13	0.241	0.243	0.256	0.249	0.193	0.186	0.000	0.262	0.270	0.286	0.223	0.219	0.000	2.629
Column total	1.746	1.723	1.636	1.688	0.578	0.582	0.000	2.777	2.788	2.993	1.837	1.827	0.000	λ = 0.120

.

4.3.4. Influence and Causation

Using the total relationship matrix, the importance and corresponding causal relationships of each barrier were calculated. The influence of each barrier was determined by summing the elements in each row of the comprehensive influence matrix, while the causality of each barrier was assessed by summing the elements in each column of the matrix. The centrality of each barrier, reflecting its significance in BIM and blockchain integration barriers, was calculated by adding the influence and causality values of each factor [52,53]. The centrality value of a barrier indicates its importance in the system. The causality of each barrier was calculated as the difference between its influence and causality, yielding both positive and negative values. These values help categorize barriers into cause and effect groups based on their positions in the system. The results of the influence index for each barrier are shown in

Table 9. The degree of the barriers’ influence.

Code	Pj	Qi	Pj + Qi	Qi − Pj	Rank Based on Pj + Qi	Group
IB1	1.509	1.693	3.201	0.184	7	Cause
IB2	1.539	1.695	3.233	0.156	6	Cause
IB3	1.411	1.611	3.023	0.200	8	Cause
IB4	1.479	1.739	3.218	0.260	9	Cause
IB5	0.555	2.114	2.669	1.559	10	Cause
IB6	0.551	2.144	2.696	1.593	11	Cause
IB7	0.000	2.474	2.474	2.474	13	Cause
IB8	2.708	0.349	3.057	−2.359	1	Effect
IB9	2.509	0.377	2.886	−2.132	3	Effect
IB10	2.706	0.394	3.101	−2.312	2	Effect
IB11	1.818	0.731	2.549	−1.086	4	Effect
IB12	1.755	0.793	2.549	−0.962	5	Effect
IB13	0.000	2.425	2.425	2.425	12	Cause

. Furthermore, a cause–effect relationship diagram was created with centrality on the horizontal axis and causality on the vertical axis, marking the position of each barrier in the diagram. The results are shown in Figure 5.

5. Discussion and Implications

5.1. Discussion

The findings of this research offer valuable insights, particularly as the ISM model identifies five distinct hierarchical levels that describe the relationships between the identified barriers. Among these, IB8, IB9 and IB10 are located in the first level, at the peak of the model. These barriers reflect the hesitation and concerns construction organizations have regarding the benefits of adopting emerging technologies. IB7 and IB13 are located in the fifth level, at the bottom of the model, serving as the fundamental factors. These barriers highlight that the key to BIM and blockchain integration lies in cost and regulation. In addition, the middle layer mainly contains barriers related to knowledge, skills training, and technology. These barriers are influenced by the bottom-level barriers and, in turn, they influence the top-level barriers. More importantly, the output of ISM shows that IB7 (High initial cost) and IB13 (Legal and regulatory) may fundamentally deter many construction companies from integrating BIM with blockchain, a finding that aligns with [3,4]. Due to the low investment in innovation in the construction industry, many companies face financial constraints, exacerbating their resistance to change. In addition, with the vigorous development of China’s new infrastructure, various government departments have realized the key role of information technology, and have introduced some favorable policies. However, since the integrated application of BIM and blockchain is still in its early stages, blockchain supervision and certain legal policies remain uncertain. This uncertainty causes many construction organizations to hesitate and refrain from investing significant resources in the digital transformation of BIM and blockchain. As highlighted in the industry report by the Institution of Civil Engineers, regulatory uncertainty is a key barrier to the early integration of BIM and blockchain [54].

The MICMAC analysis divides all barriers into four factor groups. Notably, the first quadrant, representing the autonomous factor group, contains no barriers, which further underscores the critical role of the identified barriers in the system. The second quadrant, representing the dependent factor group, includes IB8, IB9, IB10, IB11 and IB12, indicating that these barriers are more influenced by other barriers. The third quadrant, the linkage factor group, includes IB1, IB2, IB3 and IB4, which were mainly technical barriers. This highlights that technical barriers not only affect organizational or environmental factors, but also influence stakeholder trust, attitudes, and work processes. The fourth quadrant, the independent factor group, includes IB5, IB6, IB7 and IB13, emphasizing the importance of cost, regulation, expert experience, and training.

Finally, as per the DEMATEL analysis, the centrality of each obstacle was ranked, and the causal relationship of each obstacle in the whole system was determined. The results reveal that the centrality rankings of IB8, IB10, and IB9 were relatively high, indicating that these barriers play a crucial role in the system. Barriers with higher centrality are particularly important and should be prioritized. This also highlights that, in the initial stages of applying emerging technologies, the value of the technology and the attitudes of stakeholders are critical [55]. The technical barriers to BIM and blockchain integration can exacerbate this situation, especially for small and medium-sized construction companies, which often lack an understanding of how these technologies work and the value they can bring to project practice. Therefore, providing education, training, and expert guidance for employees is essential. Only with adequate organizational preparations can construction organizations successfully navigate the challenges of digital transformation and maximize the value of technology integration. In addition, IB1, IB2, IB3, IB4, IB5, IB6, IB7, and B13 were classified as the cause group, mainly influencing other barriers and contributing to the hindrance of BIM and blockchain integration. In contrast, IB8, IB9, IB10, IB11, and B12 were classified as the result group, mainly affected by other barriers and representing the effects of hindrance to the integration of BIM and blockchain.

5.2. Academic Implications

The contributions of this research are primarily reflected in the following aspects. First, although previous studies have extensively discussed BIM and blockchain integration, they have primarily focused on its advantages and potential value, with limited discussion of the barriers to such integration. This study fills this gap by systematically analyzing these barriers and employing ISM and DEMATEL methods to investigate the relationships between them. Second, although some prior studies have examined barriers to BIM and blockchain integration, most of them have focused on technical challenges in a fragmented manner, lacking a comprehensive and systematic analysis. This study has identified the barriers to BIM and blockchain integration through literature reviews, industry reports and expert feedback, not only encompassing technical barriers but also highlighting the crucial roles of cost, business processes, and human factors. By doing so, it enriches the theoretical framework for understanding the factors influencing BIM and blockchain integration. Finally, this research provides a foundation for future empirical studies and provides a theoretical basis for developing targeted intervention strategies in the future.

5.3. Managerial Implications

BIM and blockchain integration optimizes the management and execution of construction projects by improving data transparency, traceability, collaboration efficiency, and security [56,57]. However, as highlighted in this study, several challenges remain. Given that high initial costs are a major barrier, governments and policymakers should increase financial support and incentive policies for BIM and blockchain integration, particularly for small and micro enterprises, as well as for infrastructure development. At the same time, a well-defined legal and regulatory framework is essential for ensuring the sustainable development of these technologies. In addition, strengthening technical training and talent development is fundamental to the successful integration of BIM and blockchain. Establishing an industry certification system can help cultivate professionals with interdisciplinary expertise, addressing the construction industry’s growing demand for both technical and managerial talent [58]. To address concerns around technology immaturity and security risks, a unified data privacy protection and security management framework should be established. Enhancing technological maturity and strengthening security measures will mitigate technical risks associated with BIM and blockchain integration. Moreover, cross-industry and cross-departmental collaboration remains a challenge, particularly in fostering trust and adapting workflow. To overcome these barriers, industry stakeholders should build a collaborative framework, enhance communication, promote value co-creation, and facilitate cultural transformation within organizations. These efforts will help dispel skepticism toward new technologies, provide investors with a clearer value proposition, and boost investor confidence. Ultimately, such measures will accelerate the integration and widespread adoption of BIM and blockchain in the construction industry.

6. Conclusions and Limitation

6.1. Key Findings

This research mainly conducts a modeling analysis of the barriers to BIM and blockchain integration, exploring their hierarchical structure and causal relationships. Based on a comprehensive literature review and expert insights, 13 key barriers to BIM and blockchain integration have been identified and analyzed using a hybrid ISM–DEMATEL approach. The ISM analysis established the hierarchical structure of these barriers, dividing them into five levels. Among them, IB7 and IB13 were at the bottom of the hierarchy, serving as the root causes of barriers to BIM and blockchain integration. In addition, the DEMATE analysis determined the causal relationships and impacts of each barrier. These barriers were labeled as cause groups and effect groups. IB8, IB10 and IB9 emerged as the most impactful barriers in the system. By addressing the fragmentation of existing research on the factors influencing BIM and blockchain integration, this study provides a foundation for future empirical research, and offers valuable practical insights for policymakers and industry stakeholders.

6.2. Limitations and Future Directions

This research has some limitations. First, the study primarily draws on experts from China, which means the findings may be specific to the Chinese construction industry and may not directly apply to other countries or regions without modification. The barriers identified in this study could be influenced by unique cultural, regulatory, and technological factors in China. Therefore, caution should be taken when applying these findings to different contexts. Future research could expand the data collection across multiple countries or regions to allow for a comparative analysis, which would offer a broader understanding of commonalities and differences in barriers across various cultural and regulatory environments. Second, the identification and selection of barriers were mainly based on the literature and expert opinions, which may introduce biases related to the experts’ experiences and interests. To mitigate this, future research could validate these barriers through real-world case studies and explore their interdependencies more comprehensively. Finally, given that BIM and blockchain integration in construction is still in its early stages and there is a limited pool of available experts, this study did not conduct large-scale data collection for empirical tests on barrier factors. As the adoption of BIM and blockchain continues to grow, future research could employ methods such as structural equation modeling, regression analysis, or experiments to further investigate the drivers, barriers or critical success factors for the successful integration of these technologies in construction projects.