INTRODUCTION
Problem Statement
Blockchain technology (BCT) has emerged as a promising solution to revolutionize various industries, including agriculture [1], insurance [2], supply chain management
Blockchain technology is poised to be a significant computing mega-trend in the coming decade, as noted by the World Economic Forum Its transformative potential in the architecture, engineering, and construction (AEC) industry has been highlighted by various scholars, including Li et al., Hunhevicz and Hall, and Kiu et al., who emphasize its capabilities in enhancing transparency, ensuring immutability, and providing security.
The integration of blockchain technology can significantly enhance the efficiency and reliability of various practices within the Architecture, Engineering, and Construction (AEC) industry, particularly in project management and optimizing the construction supply chain.
The Architecture, Engineering, and Construction (AEC) industry, one of the largest globally, has struggled with productivity compared to other manufacturing sectors in recent decades, facing significant challenges Inefficient collaboration, information sharing, and workflow management among stakeholders hinder efficiency and performance, particularly in construction, where poor coordination can lead to supply chain inefficiencies and costly rework, costing the US construction industry an estimated $31 billion in 2018 Additionally, project progression often involves financial and informational exchanges, but off-site or overseas activities can result in management control loss, inconsistent payment terms, and unstable cash flow, leading to increased disputes over delayed payments and concerns about quality and data integrity Fragmentation and discontinuity within the AEC industry further exacerbate these issues.
The complexity of design, manufacturing, storage, transportation, and on-site assembly processes in construction projects creates a significant gap in accessible, reliable, and transparent information resources This deficiency hampers both clients and contractors in verifying the accuracy of crucial data related to stakeholder interactions, including professional qualifications, material certifications, project timelines, and quality standards Consequently, these challenges can lead to inefficiencies in construction timelines, increased costs, and compromised quality, ultimately resulting in client dissatisfaction and potential negative impacts on the national economy.
The construction industry faces significant challenges such as ineffective communication, delays, disputes, and errors Blockchain technology presents a viable solution by offering a decentralized platform for real-time tracking and monitoring of construction activities, fostering a secure environment for transactions and data exchange Additionally, smart contracts—an essential feature of blockchain—automate and enforce construction contract terms, reducing disputes and delays while ensuring timely project delivery and payment.
Blockchain technology enhances the construction supply chain by creating digital identities for materials, equipment, and personnel, thereby improving tracking and management This innovation addresses quality control and material traceability issues effectively Additionally, blockchain facilitates the development of digital twins for buildings, allowing for improved monitoring and maintenance of systems and components, while providing a valuable database for future upgrades Furthermore, integrating blockchain with Building Information Modeling (BIM) significantly boosts the efficiency of project collaboration by optimizing information management and sharing among stakeholders.
The integration and implementation of blockchain technology in the Architecture, Engineering, and Construction (AEC) industry face significant challenges that require urgent attention Key barriers include the connectivity of smart contracts with complex projects, issues related to data privacy and storage, platform selection, the costs associated with adopting blockchain systems, and uncertainties surrounding legal and regulatory frameworks Additionally, there are interconnections among these barriers that complicate the adoption process Despite the importance of these issues, the barriers to blockchain adoption in the AEC sector have not been thoroughly explored Understanding these challenges and their interrelationships is crucial for accelerating the acceptance and deployment of blockchain technology in the industry This thesis aims to address three specific research questions to shed light on these barriers.
- RQ1: What are the most significant barriers to blockchain adoption in the AEC industry?
- RQ2: What are the interrelationships and contextual ties between the barriers?
- RQ3: What is the strength of these interdependencies?
Research Objective
This thesis aims to explore the barriers to adopting blockchain technology in Vietnam's Architecture, Engineering, and Construction (AEC) industry It has three specific objectives: first, to identify key adoption barriers through literature review and expert insights; second, to model these barriers to demonstrate their interrelationships and hierarchical structure; and third, to evaluate the strength of causal relationships among the identified constructs and classify them accordingly.
Scope of Study
This thesis explores the obstacles to adopting blockchain technology within Vietnam's Architecture, Engineering, and Construction (AEC) sector, emphasizing the interconnectedness of these barriers Despite its transformative potential for the AEC industry, the slow adoption of blockchain is attributed to various challenges The study conducts a thorough analysis without imposing a time constraint on the articles reviewed, sourcing relevant literature from databases like Science Direct, SCOPUS, Web of Science, ASCE Library, Emerald, and Springer, using keywords such as "Blockchain," "architecture," "engineering," and "construction."
Master Thesis [4] Khuc Quang Trung - 2170309
"project management." In addition to databases, credible journal articles, books, and reports were consulted in order to compile an exhaustive list of barriers
This research aims to gather data from seasoned professionals and academics in Vietnam's AEC industry, specifically targeting individuals with at least ten years of tactical experience and expertise in blockchain from reputable institutions The insights gained will be analyzed to identify the key challenges hindering blockchain adoption in the AEC sector and explore their interconnections Ultimately, the study will offer recommendations to address these challenges and enhance the integration of blockchain technology in the industry.
LITERATURE REVIEW
Definitions of blockchain
Blockchain is a decentralized and immutable distributed ledger technology that records transactions among network participants According to Dounas and Lombardi, it consists of a distributed ledger, a consensus protocol, and cryptographic elements This innovative technology was created by Satoshi Nakamoto.
2008, and its most prominent application is in the Bitcoin cryptocurrency
Blockchain is defined as a distributed ledger technology (DLT) that securely records all digital transactions and events among network participants, as noted by Hamma-adama et al It operates on a peer-to-peer network, storing data across multiple devices without reliance on a central server, according to Akinradewo et al Additionally, Li et al describe blockchain as an expanding inventory of documents, known as blocks, which are cryptographically linked through hashes.
Blockchain is a decentralized network without a central authority, allowing all transactions to be visible to every node, as highlighted by Tyagi et al [27] According to Yang et al [13], there are two main types of blockchain networks: public blockchains, accessible to anyone through general consensus mechanisms, and consortium blockchains, where users are pre-identified and must follow a specified consensus process Furthermore, Abrishami and Elghaish (2019) [28] describe blockchain as a permissioned digital ledger that facilitates secure, transparent, and immutable financial transactions within the Architecture, Engineering, and Construction (AEC) industry.
Blockchain characteristic
Blockchain technology emerged to create a financial system that is transparent, decentralized, autonomous, and stable The foundation for this innovation was laid by Satoshi Nakamoto in 2008 with the introduction of Bitcoin Since its inception, blockchain has found applications across various sectors.
Blockchain technology serves as a decentralized public ledger that securely encrypts digital transactions and organizes them chronologically Each new transaction leads to an updated and verified digital ledger, establishing an unchangeable record.
In blockchain technology, when participants agree on a new block, it is added to the existing chain, facilitating the transaction process Each block contains transaction data and a hash linking it to the previous block, ensuring a secure and verifiable chain If consensus is not reached, the new block is rejected A key feature of blockchain is its decentralization, where all participants manage the network collaboratively, without a central authority, and each participant maintains a local copy of the ledger.
Block is created online representing the transaction
Block is broadcasted to all members in network to verify consensus
Approval and validation of transaction Transaction Scrapped
Block is added permanently to existing blockchain as a transparent record
Transaction between X and Y is executed
Step 2: Creation of new Block
Step 3: Broadcast of Block to network
Step 5: Block is added to existing chain
Block is created online representing the transaction
Block is broadcasted to all members in network to verify consensus
Approval and validation of transaction Transaction Scrapped
Block is added permanently to existing blockchain as a transparent record
Transaction between X and Y is executed
Step 2: Creation of new Block
Step 3: Broadcast of Block to network
Step 5: Block is added to existing chain
Figure 2.1 illustrates the step-by-step process of transactions on the blockchain, where 'X' initiates the transaction and 'Y' receives it The transaction is then shared and validated across the decentralized network, leading to the creation of a new approval block Upon consensus from network participants, this block is added to the existing chain, completing the transaction Each block serves as an entry in the ledger, containing transaction data and a hash that links it to the preceding block If consensus is not achieved, the new block is rejected.
Blockchain network
Blockchain, a form of Distributed Ledger Technology (DLT), digitizes and decentralizes transactions, functioning as a database that records and shares digital events among network participants Transactions are grouped into blocks, each featuring an immutable cryptographic signature linked to the previous block, creating a secure chain This structure enables trustless interactions in a peer-to-peer network without the need for a central authority Additionally, all transactions are visible to every node in the network, facilitating data access and verification throughout the blockchain.
Blockchain technology enables users to verify the authenticity and reliability of data by examining transaction histories across network-connected computers It allows for the creation of unique ownership certificates for land titles, providing buyers with access to property information and secure trading records, thus reducing the risk of forgery The data's immutability is guaranteed through the sequential addition of blocks, each containing a cryptographic hash of the previous block, making it impossible to alter or falsify records once they are added Additionally, the peer-to-peer network ensures that all transactions are stored immutably, and each transaction is timestamped, allowing users to easily trace past transactions through any network node Blockchain networks are categorized based on various criteria, with management and permission levels being the most common classification methods.
Both private and public blockchain networks feature decentralized architectures that facilitate peer-to-peer transactions without relying on a trusted third party Despite their similarities, these networks exhibit significant differences in their structure and use cases.
Private blockchains offer a high transaction processing rate and involve a limited number of authorized participants, enabling faster consensus and the ability to handle multiple transactions per second, unlike public blockchains which face slower processing due to the need for network-wide consensus, as seen in Bitcoin's Proof-of-Work mechanism Public blockchains also present privacy risks due to their immutable data storage and append-only nature, requiring all nodes to agree on any changes, which can delay the mining of new blocks In contrast, private blockchains emphasize data privacy and allow modifications only with unanimous consent among nodes, while public blockchains lack control over user uploads, making it impossible to reverse sensitive data submissions.
Figure 2.2 The skeleton of a private blockchain network [38]
Public blockchains offer advantages such as an unlimited number of anonymous nodes and encrypted communication, allowing each node to operate with both public and private keys, which eliminates the need for trust among users This transparency enables all network members to access and verify blockchain transactions independently, enhancing security against malicious actors In contrast, private blockchains limit access to verified parties, making them more vulnerable to manipulation due to fewer nodes Furthermore, public blockchains incur no infrastructure costs, while private blockchains require significant operational and adoption expenses, reinforcing the perception that public blockchains are more secure and cost-effective.
Figure 2.3 The skeleton of a public blockchain network [38]
Master Thesis [10] Khuc Quang Trung - 2170309
Federated blockchains blend the features of both public and private blockchains, introducing a named leader to oversee transaction verification, which creates a partially decentralized system This hybrid model strikes a balance between the low-trust environment of public blockchains and the high-trust framework of private blockchains.
Consortium blockchains, or federated blockchains, are semi-private solutions that lack a single owner, featuring a network of privileged nodes They offer several benefits similar to private blockchains, such as enhanced privacy, efficiency, scalability, and performance, but are managed by a collective group rather than an individual entity Consequently, governance is a vital aspect of consortium blockchains.
Consortium blockchains, like private blockchains, allow for restricted participant access to the ledger, enabling tailored data sets for different organizations This structure facilitates exclusive communication channels and data access for specific groups, allowing certain users to view all or selected ledger transactions through pre-approved nodes.
Today, enterprises increasingly operate across multiple networks, with the financial industry leading the way by adopting Corda R3, a prominent consortium blockchain network Similarly, the construction industry often engages in consortia or partnerships, where consortium blockchain solutions foster enhanced collaboration by building trust and transparency among participants These networks feature privileged permissioned nodes, ensuring secure and efficient operations within the consortium.
Figure 2.4 The Skeleton of a consortium blockchain network [38]
Main Blockchain platform
Ethereum, Hyperledger Fabric, and R3's Corda are key blockchain platforms, with Ethereum and Hyperledger Fabric being versatile across various industries, while Corda focuses on the financial services sector This study specifically evaluates and adopts Ethereum and Hyperledger Fabric.
Blockchain 1.0, the fundamental technology of Bitcoin, was the first implementation of Nakamoto's [43] concept of blockchain technology This initial implementation of blockchain was utilized predominantly for cryptocurrencies 2015 saw the introduction of Blockchain 2.0, which brought the concept of smart contracts to the forefront of the industry Ethereum, the second public blockchain platform, was created as a consequence of this evolution [10]
Ethereum, proposed by Vitalik Buterin, is a blockchain-based distributed computation platform that enhances the decentralization of transactions and markets, broadening the potential applications of blockchain technology This platform supports both public and private transactions and is inspired by Bitcoin Utilizing a proof-of-stake (PoS) consensus algorithm, Ethereum offers a more efficient alternative to the traditional proof-of-work (PoW) model While PoW relies on game theory and cryptographic algorithms to determine transaction validators, PoS selects participants based on the proportion of stakes they hold, promoting a more energy-efficient and scalable blockchain environment.
Master Thesis [12] Khuc Quang Trung - 2170309
Ethereum is a versatile blockchain platform that allows users to create cryptocurrency applications and execute smart contracts It features two types of accounts: third-party accounts, which are controlled by individuals through private keys, and contract accounts, which are managed by code Users can initiate transactions and interact with external accounts by invoking contract functions, while contract accounts operate through interactions with other accounts.
Solidity, a popular scripting language, is commonly used for developing contract account protocols These protocols are then compiled into a stack-based programming language for contract account execution [46]
Hyperledger Fabric, launched by the Linux Foundation in early 2016, is a collection of blockchain frameworks and tools It is part of the Hyperledger consortium, which includes IBM and various other organizations, aimed at promoting the development of blockchain-based applications across diverse industries.
[35] Hyperledger Fabric, a private blockchain platform that is modular and permission, is regarded as one of the most developed blockchain platforms to date
It is the first of its kind to facilitate smart contract execution in multiple general- purpose languages for programming, including Node.js, Java, and Go [47]
The execute-order-validate architecture of Hyperledger Fabric distinguishes it from other blockchain platforms by streamlining the transaction flow, which includes execution, prioritization, and validation Unlike public blockchains, each node within Hyperledger Fabric possesses a unique identity and can fulfill specific functions.
- Clients: They propose and submit transactions for ordering
- Peers: These individuals execute transaction proposals, validate transactions, and maintain blockchains
- Orderers (Ordering Service Nodes): These nodes aggregate client transactions and determine the order of all transactions
In addition to these nodes, Hyperledger Fabric includes the following design components:
- Membership Service Provider (MSP): The MSP is responsible for administering user identities and regulating network access using a certificate authority (CA) for user validation and authentication
A smart contract, or chain code, defines the duration of a global asset and encompasses mechanisms for asset modification and state querying As a crucial component of Hyperledger Fabric, the chain code integrates multiple smart contracts for deployment on specific channels.
Transactions represent consumer-initiated changes to an asset within the current world state Each transaction can either read from or write to the global state and requires validation in accordance with the endorsement policy set by the chain code.
- This is an immutable log of all channel-based transactions An authorized user can observe the complete transaction history of an asset by querying the ledger
- The current status of each asset in the ledger You can obtain the current status of an asset by inquiring about the global state
Channels: Hyperledger Fabric permits the construction of distinct channels, each of which provides an independent communication layer for a subset of participants, ensuring the privacy of communication and data [13].
Blockchain technology in the AEC industry
Blockchain technology could transform the construction industry, with researchers investigating its implementation and benefits Current studies in the Architecture, Engineering, and Construction (AEC) sector primarily concentrate on two key areas.
Qualitative and theoretical methods have been employed to assess blockchain research within the Architecture, Engineering, and Construction (AEC) industry For example, Perera et al investigated the potential of blockchain technology in construction, concluding that it significantly impacts the industry rather than being just a novelty Similarly, Li et al identified key research topics related to blockchain in the built environment by analyzing recent technological advancements and literature, offering a detailed overview of the challenges and opportunities presented by this technology.
Master Thesis [14] Khuc Quang Trung - 2170309 findings into a framework comprised of two multidimensional conceptual models to develop a road map for deploying blockchain in the construction industry McNamara and Sepasgozar
A comprehensive literature review identified 46 relevant studies on the development of intelligent contracts in the construction sector Dounas and Lombardi (2019) explored the integration of blockchain technology in the construction industry, highlighting its potential to improve transparency, trust, and accountability in construction processes Additionally, Abrishami and Elghaish proposed a permissions-based blockchain framework aimed at transforming the financial system within Architecture, Engineering, and Construction (AEC) during project delivery, emphasizing the benefits of enhanced financial transparency, reduced disputes, and increased confidence in construction projects.
Recent studies have highlighted the adoption and benefits of blockchain technology in the Architecture, Engineering, and Construction (AEC) industry Research by Chong and Diamantopoulos, Das et al., and Ahmadisheykhsarmast and Sonmez suggests that smart contracts and blockchain can create a secure and transparent platform for construction transactions Qian and Papadonikolaki propose that blockchain can enhance data monitoring and resource transfer within supply chain management Wang et al (2020) emphasize that blockchain improves information exchange, real-time control, and traceability in precast construction Additionally, Elghaish et al point out that distributed ledger technology can streamline automated financial transactions in collaborative projects Penzes identifies key advantages of blockchain, including improved supply chain management, increased transparency, and reduced disputes in the construction sector.
Li et al (2019) conducted a systematic evaluation of blockchain technology in the construction industry, identifying key use cases such as project management, supply chain management, and digital identity They also proposed conceptual models for integrating blockchain into construction processes Meanwhile, Yang et al explored the use of both private and public blockchain to improve the integration of construction business processes and data, revealing that both types enhance transparency, traceability, and security in construction operations.
The integration of Blockchain with digital technologies like IoT, BIM, AI, and Big Data aims to improve construction information management Particularly, the combination of distributed ledger technology with BIM is gaining attention, as BIM serves as a collaborative platform for architects, engineers, and construction professionals However, a major challenge is the fragmentation of data across various BIM models, leading to inconsistent management and unclear ownership Blockchain's centralized nature can address these issues, potentially transforming the construction industry Research by Xue and Lu has introduced a model that reduces redundant information in BIM and Blockchain integration, while Sinenko et al proposed a secure access solution for professionals through blockchain Although the transition to a BIM-based system is hindered by fragmented model management, smart contracts can enable secure access to BIM models and record all transactions on the blockchain Ye et al recommend that only specific transactions be converted to smart contracts within a BIM workflow to alleviate the technical burden on project teams.
The integration of blockchain with digital technologies like the Internet of Things (IoT) and artificial intelligence (AI) fosters beneficial advancements Research by Suliman et al demonstrates that blockchain can automate payments and monetize IoT data without intermediaries, enhancing trustworthy data sharing and preventing deception in IoT interactions Additionally, the application of blockchain within AI is being explored, as highlighted by Salah et al (2018), who review emerging blockchain applications in this field.
AI as well as their potential implementations in explainable AI, digital twins, automated
Khuc Quang Trung's master thesis explores the integration of machine learning, hybrid learning models, and lean augmented data learning K Tyagi et al investigate the potential of blockchain and the Internet of Things (IoT) within Industry 4.0 and Society 5.0, highlighting the challenges and opportunities presented by this integration They emphasize the benefits, such as enhanced security, transparency, and efficiency across various sectors including agriculture, healthcare, transportation, and logistics, supported by case studies demonstrating practical applications The authors conclude that blockchain and IoT can drive significant transformations in industries and society Additionally, the electronic document management (EDM) platform, essential for the construction industry, has evolved prior to blockchain's emergence Blockchain technology presents a secure and cost-effective alternative to traditional EDM systems, enabling comprehensive data storage throughout a construction project's lifecycle This technology allows for the secure storage of relevant design documents in a decentralized environment, requiring confirmation from blockchain participants for specific records.
Recent studies have extensively explored blockchain technology within the Architecture, Engineering, and Construction (AEC) industry, yet its applications and research remain nascent compared to the vast potential and challenges it presents This article conducts a comprehensive literature review to highlight the barriers hindering blockchain adoption in the AEC sector.
Barriers to the adoption of blockchain in the AEC industry
A comprehensive review of prior research on blockchain technology (BCT) within the Architecture, Engineering, and Construction (AEC) industry was conducted to pinpoint potential barriers to its adoption This analysis focused on key terms like "barriers," "difficulties," "problems," and "challenges." Although there is existing research on blockchain adoption in the AEC sector, many studies tend to overlook the interconnections between these barriers, primarily focusing on listing general obstacles to BCT implementation.
Recent research has explored the potential of blockchain technology (BCT) in various sectors, particularly in construction Perera et al examined its applications in supply chain management, payment processing, and data sharing, while Li et al provided a thorough evaluation of blockchain in construction, proposing a model for its integration that emphasizes benefits such as transparency, collaboration, and cost savings Yang et al conducted a pilot study highlighting the integration of private and public blockchains, addressing both benefits and challenges Additionally, Teisserenc and Sepasgozar introduced a framework for combining BCT with Digital Twin technology in the context of Industry 4.0 for the Building, Engineering, Construction, Operations, and Mining (BECOM) industries While many studies have identified barriers to blockchain adoption in the Architecture, Engineering, and Construction (AEC) sector, only a few, including those by Cheng et al., Li et al., and Xu et al., have explored the interdependent relationships among these barriers, revealing a need for a more comprehensive analysis.
Most previous research on blockchain technology has relied on survey data or literature reviews, limiting insights due to the nascent stage of the technology and the small pool of knowledgeable individuals Furthermore, there has been a notable lack of analysis concerning the interrelationships between barriers and the direct and indirect connections among various obstacles from different stakeholder perspectives This study seeks to address these gaps in the existing literature.
The authors identified and selected key barriers to the implementation of behavior change techniques (BCT) from previous research These barriers were chosen for their clarity and ease of understanding, ensuring that they effectively communicate the challenges faced in implementing BCT.
Master Thesis [18] Khuc Quang Trung - 2170309 overlap Table 2.2 summarizes the identified barriers and the frequency with which they were cited
Table 2.1 Characteristics of previous studies
Articles Theme Type of Study
Challenges and Opportunities when Adopting BCT in the
S Perera et al [38] Analyzing the application potential of blockchains in construction
Assessing the current state of distributed ledger technologies (DLT) in the built environment Proposing conceptual models for implementing blockchain in the construction industry
Literature review/ conceptual/ practical case study
[27] examining the potential benefits and challenges of combining blockchain technology and the Internet of Things (IoT) for Industry 4.0 and Society 5.0
B Penzes [53] overview of the potential applications of blockchain technology in the construction industry
Identifying the determinants of blockchain adoption in the construction industry and verifying the influence of the combination of various factors on adoption intention
Y Xu et al [64] provide a comprehensive understanding of BCT adoption barriers and their interdependent relationships in the context of the AEC industry
S Sepasgozar [14] proposes the use of blockchain technology and digital twins in the construction industry
O I Akinradewo et al [26] exploring the barriers to the implementation of blockchain technology in the construction questionnaire survey
Reviewing the current status of blockchain applications via a bibliometric analysis combined with a systematic literature review
R Yang et al [13] exploring the feasibility of applying both public blockchain and private blockchain technologies in the construction industry Practical case studies Hamma-adama, et al [25], for example, figured out three main challenges including the level of preparedness in technology, policy, and awareness of blockchain utilization in the construction industry Similarly, the study conducted by B Penzes et al emphasized that incompleted regulations and fragmentation in construction projects will be major challenges for BCT adoption Another example is the study from O I Akinradewo, et al [26] ranked the most significant barriers, which included a lack of clarity, scalability risks, a lack of skills or knowledge, social acceptance, and a lack of standardization In order to clarify how respondents perceive these barriers to the implementation of blockchain technology in the built environment, they were subsequently categorized into three groups: organisational barriers, social barriers, and technological barriers
Research by Xu et al identified that low technology levels and regulatory concerns are significant barriers in the AEC industry, primarily due to traditional cultural influences The study categorized seven barriers into two groups: the cause group, which includes lack of IT infrastructure, legal uncertainty, insufficient knowledge and expertise, security issues, and scalability challenges; and the effect group, comprising lack of trust among stakeholders, high initial investment costs, reluctance from business owners, and inadequate collaboration, which are influenced by other factors.
Managers in the construction industry are hesitant to adopt blockchain technology due to several challenges, including high initial costs for installation and maintenance, which can deter decision-makers Concerns about data security, particularly the risk of unauthorized monitoring or misuse, further inhibit blockchain adoption Additionally, the involvement of multiple stakeholders often leads to conflicting objectives and increased project complexity, complicating the implementation process Research utilizing the Technology – Organisation – Environment (TOE) framework identifies significant barriers such as the complexity of construction projects, the costs associated with blockchain adoption, and a lack of knowledge and skills Most construction companies rely on high-tech firms for blockchain solutions, creating dependencies that pose additional obstacles to independent development and integration of this technology.
Support from top management is essential for the successful adoption of blockchain technology, as emphasized by Khuc Quang Trung in their master thesis Additionally, various researchers have identified barriers to the adoption of blockchain technology, which are detailed in Table 2.2.
Table 2.2 List of Barriers, descriptions, and references
Business owners often hesitate to invest in new technologies due to their perception of risk and uncertainty, viewing them as untested and not widely adopted This reluctance can stifle innovation and hinder progress within the industry, as resources are not allocated to potentially transformative solutions.
The decentralized nature of Blockchain makes it difficult to guarantee the privacy and security of sensitive data such as building plans, contracts, and financial information
Many countries are unprepared to embrace blockchain technology due to insufficient policies, regulations, and oversight, leading to legal and compliance risks for stakeholders This uncertainty fosters reluctance to invest in blockchain, ultimately hindering its adoption across various industries.
The absence of essential technical expertise and robust IT infrastructure hinders the effective implementation and maintenance of blockchain solutions This limitation restricts the potential advantages of blockchain technology and fosters a hesitance to allocate resources for its adoption.
The high costs of developing, deploying, and maintaining blockchain solutions pose significant challenges for many stakeholders, often restricting adoption to large-scale projects with ample budgets This financial barrier hinders smaller projects and stakeholders from leveraging blockchain technology effectively.
Many stakeholders may not fully understand the benefits and risks associated with blockchain applications, leading to hesitance in investing time and resources into the technology This lack of awareness ultimately hinders industry motivation and enthusiasm for adopting blockchain solutions.
Concerns about dependence on blockchain operators and high-tech companies raise issues related to trust, security, and data privacy These fears hinder the full realization of the benefits associated with decentralization and transparency that blockchain technology promises.
Lack of collaboration among stakeholders
To establish an effective blockchain network, it is crucial for all stakeholders to engage and recognize the value of blockchain technology The implementation phase often presents challenges related to collaboration, interaction, and organization among participants.
Construction project's complexity and fragmentation
Research gap
The Architecture, Engineering, and Construction (AEC) industry stands to gain significantly from the integration of blockchain technology, which is increasingly recognized for its transformative potential Understanding the interdependencies among the barriers to blockchain adoption is essential for effective decision-making in its implementation Although previous research has identified these barriers and suggested quantitative hypotheses, there is a notable lack of studies that explore and define the interconnected relationships between these obstacles within the AEC sector.
The increasing significance of blockchain technology in the Architecture, Engineering, and Construction (AEC) industry highlights the potential for transformative benefits and the need for its integration into current infrastructures Understanding the interdependencies among various barriers is essential for effective decision-making regarding blockchain implementation Although previous research has addressed these barriers and suggested quantitative hypotheses, there is a notable lack of studies that theorize and define the interconnected relationships between these obstacles to blockchain adoption in the AEC sector.
Mathivathanan et al [3] examine blockchain adoption in the supply chain industry, highlighting qualitative correlations between barriers and their effects but lacking quantitative precision Similarly, Xu et al [64] utilize the ISM–DEMATEL method to analyze direct relationships between blockchain adoption barriers in China; however, their study fails to provide a thorough understanding of the interdependencies among these barriers.
Master Thesis [22] Khuc Quang Trung - 2170309
Atul Kumar Singh utilized fuzzy DEMATEL and social network analysis (FDSNA) to examine the obstacles hindering the implementation of blockchain technology in sustainable construction Although FDSNA provides valuable insights, it falls short of the depth and clarity offered by TISM in exploring the intricate relationships among these barriers.
Akinradewo et al [26] made a notable contribution by analyzing the barriers to implementing blockchain technology in South Africa's built environment However, their study did not explore the interrelationships among the identified barrier categories, and its findings were restricted to the context of a developing nation.
Current research highlights important findings but lacks a comprehensive theoretical framework that integrates both qualitative and quantitative approaches This gap hinders a clear understanding of the intricate relationships between the obstacles to blockchain adoption in the Architecture, Engineering, and Construction (AEC) sector.
This thesis aims to fill research gaps by creating a performance model that addresses eleven barriers to blockchain adoption in the AEC industry Utilizing the TISM method, which emphasizes contextual relationships and integrates insights from industry experts, the model offers deeper insights compared to frameworks like the Diffusion of Innovation (DOI) theory and the Technology-Organisation-Environment (TOE) model While TOE examines organizational factors affecting technology adoption, DOI is focused on predicting a firm's openness to new technologies based on innovation and organizational traits.
The obstacles identified in the study are categorized through MICMAC analysis, which assesses the interrelationships among these barriers It is essential to understand that these relationships vary in strength, directly impacting the model's effectiveness By analyzing the dominance and interdependence of each barrier, MICMAC aids in categorizing them and highlights critical factors influencing the structural model Subsequently, the final TISM model undergoes evaluation by a second group of industry experts, following the outlined procedures.
This study utilizes the DEMATEL method, an analytical approach based on graph theory and matrix tools, to tackle complex societal issues characterized by ambiguous connections DEMATEL is favored for its effective use of expert knowledge and experience By employing graph theory, this research aims to identify cause-and-effect relationships among barriers and evaluate their significance A comprehensive analysis will be provided in the following section.
Master Thesis [24] Khúc Quang Trung - 2170307
METHODOLOGY
TISM model
Various multi-criteria decision-making (MCDM) methods are utilized to illustrate key concepts in the literature, with interpretative structural modeling (ISM) being the most widely used In contrast to other MCDM techniques like Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), ISM offers a unique framework for analysis.
ISM enhances model precision by minimizing expert bias, as it does not necessitate intentionality in the links between constructs However, critics argue that ISM applications overlook important transitive relationships and primarily concentrate on nodes, resulting in a limited understanding of the connections between them.
[68] Total Interpretive Structural Modelling (TISM), which eliminates the shortcomings of ISM, is an appropriate method for addressing such challenges
TISM represents an advanced evolution of traditional ISM, specifically designed to create a contextual relationship-based performance model that addresses the challenges of implementing blockchain technology in the AEC industry This interpretive modeling approach focuses on the perceptions of group members regarding the interactions among various factors The TISM method effectively visualizes the interconnections between elements, utilizing directional indicators to demonstrate the hierarchical structure and relationships among components By positioning influential barriers within a diagram, TISM aids in their identification, while contextual connections are denoted along connecting arrows This enhancement over standard ISM incorporates critical thinking to trace transitive relationships, evaluating their actual causes based on expert insights and only considering effective transitive relationships in the model development process The essential stages of the TISM methodology are clearly outlined and illustrated.
Step 1: define and explain the barriers whose interrelationship must be determined via the literature or a group of subject matter experts
Step 2: Determine the contextual and reciprocal relationship between the identified elements The relationship is dependent on the form of structure, such as mathematical or procedural dependency, augmentation of attributes, priority, and purpose For instance, the contextual link between the two characteristics could be "P facilitates the achievement of R"
Step 3: Analyze the relationship between the criteria/elements Although the mutual relationship provides insight into the nature of the relationship, it does not explain "how" the relationship functions In the TISM method, it is necessary to establish the relationship's interpretation, and it should be noted that the interpretation will be explicit and specific for each pair of elements
Step 4: Interpret the rationale for pairwise comparison The concept of interpretative matrix offers a comprehensive comprehension of paired comparison by focusing on the action of a directed connection In a comparable evaluation, each factor is independently equated to the ith component The input for each link (i-j) may be 'Y' (Yes) or 'N' (No); if 'Y', it is evaluated further This conventional approach to database interpretation based on tables
Step 5: Create a reachability matrix and confirm the existence of transitive relationships The 'Y' or 'N' knowledge base items are converted to a binary matrix ('Y' equals 1 and 'N' equals 0) The transitivity of the formed reachability matrix must then be evaluated If a factor or variable 'A' has an effect on 'B,' and 'B' has an effect on 'C,' then 'A' can also have an effect on 'C.' The ultimate reachability matrix (FRM) is this matrix
Step 6: Separate the reachability matrix through multiple layers Determine the reachability, antecedent, and intersection sets for each barrier using the FRM A barrier with identical values in the reachability and intersection sets must be eliminated and moved to the summit of the hierarchy In order to obtain varying levels of elimination, all assignments were repeated in a similar manner
Step 7: Build the diagram by organizing all of the factors based to their exclusion levels, followed by the creation of directed links Notably, essential transitivity links can be reserved
Master Thesis [26] Khúc Quang Trung - 2170307
Step 8: Generate the interaction matrix by converting the final digraph toward a binary matrix representing all pertinent interactions Insert a '1' to denote the connection in the binary matrix, followed by the translation statement in the interpretative matrix
Step 9: Construct the TISM model employing its outcome diagram and interaction matrix The TISM model should emphasize the interpretations of matching comparisons in conjunction with the hierarchical structural model
TISM has been utilized in various studies to develop hierarchical models, investigate enablers and barriers, and analyze the interrelationships of factors in different industries
The study conducted by Yadav developed a hierarchical model using TISM for strategic performance management in Indian telecom service providers, revealing key success factors and their interactions Similarly, Jayalakshmi and Pramod applied TISM to explore enablers of a flexible control system in industry, resulting in a robust decision-making framework Additionally, research by C et al demonstrated TISM's application in analyzing the drivers and barriers of integrated solid waste management However, the TISM method has yet to be explored in the Architecture, Engineering, and Construction (AEC) industry This study identifies critical drivers and barriers along with their interrelationships, offering a strategic framework for effective policymaking.
Wuni and Shen (2019) utilized TISM to develop a comprehensive review and conceptual framework for offsite construction, identifying ten critical drivers that affect its adoption and implementation while examining their interconnections In a more recent study, Mathivathanan et al applied TISM to investigate the barriers to blockchain technology adoption in business supply chains, identifying twelve significant barriers and analyzing their interrelationships.
TISM has been utilized to explore the dimensions and interconnections of flexible manufacturing systems and sustainable supply chain performance, as demonstrated in the research by Dubey and Ali (2014) This study highlighted critical dimensions and examined their relationships, offering valuable insights for enhancing performance In a similar vein, Shibin et al have contributed to this area of research.
[77] and [74]used TISM to identify the enablers and barriers of flexible green supply chain management and integrated sustainable solid waste management, respectively
Recent studies have highlighted critical factors and their interrelationships, offering valuable insights for enhancing performance and sustainability Notably, Sindhwani and Malhotra employed TISM to develop a framework aimed at improving agile manufacturing systems, further demonstrating TISM's effectiveness in analyzing complex systems This approach provides strategic insights that aid decision-making across multiple domains.
The application of TISM in recent studies highlights its effectiveness in developing conceptual models for complex systems, especially in examining the barriers and interactions related to blockchain technology in the construction industry of developing countries like Vietnam Unlike other decision-making methods such as AHP or ANP, TISM effectively identifies critical factors and their interrelationships, aiding in policy development and informed decision-making Therefore, the ongoing use of TISM in future research is promising for enhancing the understanding of complex systems and improving decision-making across various fields.
MICMAC model
When analyzing barriers to blockchain technology implementation, it is crucial to understand their varying strengths and interdependencies The MICMAC method serves as a valuable tool, offering insights into each barrier's driving force and dependence by analyzing their interactions Additionally, the Final Reachability Model (FRM), part of the TISM method, highlights these dynamics through a power matrix divided into four categories: 'autonomous,' 'dependent,' 'linkage,' and 'driving.' This categorization helps identify factors with weak influence and dependence, those with low influence but high reliance, elements with significant driving power and dependency, and factors that exhibit strong driving power with minimal reliance Utilizing the MICMAC method enables a comprehensive understanding of these barriers.
Master Thesis [28] Khúc Quang Trung - 2170307 method and the FRM, the barriers to implementing blockchain technology can be systematically analyzed, revealing insights into their relative strengths and interdependence.
DEMATEL model
The DEMATEL technique has gained popularity in recent years, particularly when combined with TISM, offering significant advantages over traditional methods like AHP, ANP, and SEM While ANP enhances prediction accuracy in networks with dependent criteria, it fails to reveal interrelationships between variables, a strength of DEMATEL This graph theory-based approach utilizes quantitative methods to analyze complex causal relationships, making it more effective than ANP in illustrating these interconnections Additionally, unlike SEM and regression, which require strict parametric assumptions, DEMATEL can assess interactions and causal linkages with minimal data It transforms the causal aspects of complex systems into comprehensible structural models, ranking variables by importance Given the intricate and interdependent barriers to blockchain adoption in Vietnam's AEC industry, the integration of DEMATEL and TISM is especially suited for analyzing these relationships and providing actionable insights.
Table 3.1 A comparison of DEMATEL with AHP/ANP/SEM [83]
DEMATEL includes determining the causal interactions between variables based on their cause-and-effect groupings
AHP does not give interdependencies between and among variables; rather, it is used to establish the hierarchical structure of variables
ANP may offer interdependencies between and among variables; however, its complexity makes it less popular
SEM is an a priori approach mostly utilized for the creation of theory SEM needs a high sample size
The DEMATEL method includes the following steps:
Step 1: Identify the criteria to be modeled
Step 2: Create a matrix that demonstrates the direct link between the criteria Request that specialists populate the tables considering the following: No association; minimal impact; moderate influence; significant influence Then, calculate the mean of all the entries Let m represent the variable, and let A represent the average matrix
Step 3: Normalise the direct relation matrix i‟ and „j‟ for variables of row and column respectively The normalized direct relationship matrix „D‟ can be found from „A‟ by multiplying it by „S,‟ where „S‟ is:
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Step 4: Create the total relation matrix (T) based on the following relationship: where „I‟ is the identity of the matrix
Step 5: Determine the values 'D' and 'R' that represent the sum of rows and columns, respectively, to generate the causal diagram These are computed using the following relations: 'D+R' is the 'prominence' (vector along the horizontal axis) that indicates the relative importance of each factor, such as it ranks the factors In contrast, 'r-c' is known as relation.' If the 'D-C' value is positive, the criteria correspond to the 'cause' group; if it is negative, it belongs to the 'effect' group
Step 6: Determine the threshold value ( ) by calculating the mean of the 'T' matrix The threshold value facilitates the elimination of irrelevant relationships Significant values greater than '' are chosen to demonstrate the cause-and-effect relationship [83].
Delphi technique
The Delphi technique is an innovative research method designed to build consensus among expert respondents through a structured survey process Unlike traditional surveys, it requires participants to meet specific expertise criteria and involves multiple rounds of anonymous feedback This iterative approach allows facilitators to monitor interactions while keeping identities confidential, encouraging experts to reevaluate their initial opinions based on group trends Each round analyzes expert responses, sharing insights like response medians and extreme perspectives in subsequent rounds, ultimately guiding participants towards a collective agreement.
In fields like Construction Engineering and Management (CEM), where traditional research methods can be challenging due to dynamic work environments and safety hazards, the Delphi method offers a viable alternative This approach can effectively assess the impact of safety interventions on active construction sites while ensuring that employees remain protected from potential risks.
Table 3.2 highlights the number of rounds as well as measures of consensus used in previous CEM research
Table 3.2 Characteristics of Delphi Studies in CEM Research [87]
Specific prequalified 3 14 Mean Standard deviation del Caủo and de la Cruz _2002_
Specific, not prequalified 1 20 None indicated
None indicated de la Cruz et al
Specific not prequalified 1 20 None indicated
Specific, prequalified 2 12 Mean Standard deviation Gunhan and
Specific, prequalified 2 12 Mean Standard deviation Hyun et al
In 1991, a study highlighted the importance of consensus in the Delphi technique, revealing that most changes in responses typically happen between the first and second rounds of a Delphi survey.
The modified technique can significantly streamline the process by offering panelists a schedule of events and achieving early group consensus, potentially reducing the number of cycles to just two However, if the number of rounds exceeds four, response rates may decline, which can place an undue burden on participants.
When interpreting statistics based on an interval scale, it is crucial to consider the standard deviation as it reflects data dispersion and indicates panel agreement; a small standard deviation suggests consensus, while a large one signals disagreement However, Murphy et al argue that the median and Relative Interquartile Range (RIR) are more dependable indicators of consensus, as highlighted in their analysis of clinical guideline development methodologies The RIR measures the change in spread between lower and upper quartile values relative to the mean for specific success criteria.
Master Thesis [32] Khúc Quang Trung - 2170307
This thesis will employ the median as the primary statistical measure to streamline the indicator set, with the cut-off level determined by the Relative Inter-quartile Range (RIR) A higher mean indicates greater importance of the criterion, while a smaller RIR value reflects stronger consensus among experts regarding success criteria To ensure the final model includes only highly significant variables with expert agreement, a pairwise barrier is deemed significant if its mean value exceeds 2 on a 0 to 4 scale, coupled with an RIR value of less than 0.5, indicating robust expert consensus.
Development of the integrated TISM-DEMATEL-MICMAC
The framework adopted in this thesis is illustrated in
Identifying barriers for Blockchain adoption in the construction industry by literature review and expert opinion
Defining pair-wise contextual relationships between Barriers
Binary interpretation of pair-wise comparisions
Reachability matrix and transitivity check
Development of interaction matrix, interpretive matrix
Calculating the Initial Direct Influence Matrix
Deriving the Total Direct/Indirect Influence Matrix
Setting the Threshold Value and Obtain the Inner dependency matrix
Integrating TISM- DEMATEL based model
Calculation of Driving power and Dependence
Scatter plot of Barriers based on Driving power and Dependence
Analysis of Barriers for Blockchain adoption
Conclusion of influential Barriers for Blockchain adoption in the construction industry
Figure 3.1 with fourteen different steps:
Master Thesis [34] Khúc Quang Trung - 2170307
Identifying barriers for Blockchain adoption in the construction industry by literature review and expert opinion
Defining pair-wise contextual relationships between Barriers
Binary interpretation of pair-wise comparisions
Reachability matrix and transitivity check
Development of interaction matrix, interpretive matrix
Calculating the Initial Direct Influence Matrix
Deriving the Total Direct/Indirect Influence Matrix
Setting the Threshold Value and Obtain the Inner dependency matrix
Integrating TISM- DEMATEL based model
Calculation of Driving power and Dependence
Scatter plot of Barriers based on Driving power and Dependence
Analysis of Barriers for Blockchain adoption
Conclusion of influential Barriers for Blockchain adoption in the construction industry
Step 1: Identifying and defining the elements: designing the sample and data collecting is the first phase The thesis starts with a comprehensive review to identify and define numerous barriers that negatively affect the application of blockchain technology in the AEC industry Those barriers are the TISM components whose relationships have to be modeled The literature identifies the various potential barriers; in this case, there are eleven factors including The reluctance of the business owner (B1), Data privacy/security concerns (B2), Regulatory uncertainty (B3), Lack of Information Technology (IT) infrastructure (B4), High implementation cost (B5), Uncertain benefits (B6), Dependence on Blockchain operators(B7), Lack of collaboration among stakeholders (B8), Construction project's complexity and fragmentation (B9), Scalability issues (B10), Lack of expertise and knowledge (B11) These barriers are validated by experts‟ perspective for suitable the AEC industry in the Vietnamese context
Step 2: This study's subsequent phase involves defining the contextual relationship between the enumerated barriers To accomplish this, Using the opinions of experts, pairwise contextual relationships are identified between each of the barriers In this thesis, the author documents how Barrier (B1) affects Barrier (B2) and so on in the knowledge base matrix using expert discussions and Yes/No responses with accompanying logical justifications to construct the interpretive knowledge base Based on expert perspectives, the contextual relationship between each pair of barriers is determined To analyze pairwise relationships between barriers, sessions of brainstorming and/or personal interviews with experts are employed Experts are tasked with constructing the knowledge base in the type of a table, with each entry indicating the comparable pair of barriers and their extant contextual relationship, if any
Due to the challenges of conducting group brainstorming sessions, this thesis chose to interview each expert individually Each expert received an overview of blockchain technology, including its basic concepts, applications, and benefits, followed by a detailed explanation of the eleven barriers During the structured interviews, experts answered paired relationship-based questions, providing their reasoning for each response All interviews were organized to ensure consistency in questioning, and responses were recorded and transcribed for analysis It is important to note that discrepancies in expert opinions exist; for example, Expert 1 believes there is a contextual relationship between barriers B1 and B2, while Expert 4 disagrees.
Master Thesis [36] Khúc Quang Trung - 2170307
In the second and third survey sessions of this thesis, the Delphi method is employed to investigate the direct interactions between identified barriers Experts evaluate the strength of these interactions on a scale from 0 (no impact) to 4 (very strong impact) Mean and RIR indices are then calculated, leading to the removal of barrier pairs with a mean value below 2 Factors that achieve a mean value above 2 and an RIR below 0.5 are deemed to have significant impact and will be updated in the knowledge matrix Pairwise interactions with a mean above 2 but an RIR exceeding 0.5 will proceed to the third survey, continuing the Delphi technique's iterative process.
Step 3: Interpretation of pair-wise comparisons as binary: The logical explanation of the Yes/No relationship between the compared barriers is expressed as a 'n x n' matrix, where n represents the number of barriers considered in the study For each (i, j) cell, either '1' or '0' is entered based on the influence of barrier Bi over barrier Bj, with '1' indicating the presence of an influential relationship between Bi and Bj and '0' indicating the absence of such a relationship
Step 4: Transitivity verification and reachability matrix: If Bx affects By and By affects
The initial reachability matrix, derived from the logical interpretation of Yes/No connections, is analyzed for potential transitive links, as outlined by the transitivity rule Each identified transitive connection is labeled as a "transitive link," with its explanation enriched by relevant relationship elements For example, barriers B2 and B7 illustrate transitive links that, despite lacking a direct association in the original matrix, reveal that security and privacy concerns hinder enterprises' trust in blockchain providers, thereby impacting adoption Only those transitive connections with meaningful interpretations are examined further, while others are excluded The binary interpretation underpins the initial reachability matrix based on direct connections.
The transitivity test reveals multiple indirect relationships, leading to the generation of a final reachability matrix Through expert opinions gathered in the third round of surveys, the explanations for these transitive links are clarified Each derived transitive connection is examined individually, allowing for the deduction of their probability and strength, followed by an update to the knowledge base.
Direct links are shown in blue, while indirect links appear in green, with the barriers facilitating transitive connections illustrated in the adjacent matrix For example, Barrier B9 establishes the transitive relationship between B1 and B5, indicated by the number nine in the connection cell Additionally, Barriers B8 and B9 contribute to the transitive connection between B1 and B3, as per the transitivity rule The barriers responsible for each transitive connection have been identified, and expert opinions gathered during the third round of surveys in this thesis provide clarity on these transitive links Specialists are consulted to discuss each derived connection, allowing for the assessment of the probability and strength of transitivity, leading to an updated knowledge base.
Step 5: Level partitions: Comparable to ISM, the partitioning of levels identifies the barrier arrangement on a level-by-level basis The reachability set, antecedent set, and junction set are calculated and arranged in a table based on the driving power and dependent power of each barrier The boundaries at the apex of the hierarchy are not extended above their own level Using an ISM-like iterative method, the levels of each barrier are determined The digraph and TISM models are based on these levels The preceding phase produces the final reachability matrix, which is comprised of entries on pairwise evaluations due to direct connections and a few entries derived from inferred transitive securities, the effective transitive relationships The final reachability matrix is used to produce the reachability, antecedent, and intersection sets for each barrier The barrier whose intersection set and reachability set is identical is categorized as having the highest level (Level I), and the Level I barriers are removed from the next iteration table This procedure is repeated until each barrier has been designated at the appropriate level
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This iterative procedure is depicted in the Levels partition of blockchain adoption barriers in the AEC industry table; after four iterations, all component levels are determined
Step 6: In the final reachability matrix, the barriers are positioned visually based to their levels, and the links between them are represented by arrows [78] To depict direct connections with continuous paths, a simplified version of the digraph is first created After evaluating the transitive connections, only those that have been determined to be effective and have a significant relationship are illustrated with indistinct arcs in the digraph [78] This is the final TISM model that was obtained In our case, making a digraph necessitates visually arranging each of the nine barriers in accordance with the level partitions Arcs depict the connections between obstructions based on the correlations detailed in the final reachability matrix Figure 6 depicts the produced hierarchical model, where continuous arcs represent direct relationships and discontinuous arcs represent transitive connections
Step 7: Interaction matrix and Interpretation matrix On the basis of the diagram, a binary interaction matrix is constructed by translating all interactions designated as "1" and leaving the remaining cells vacant [80] The effective transitive linkages are represented as 1* With the assist of the knowledge base, a matrix containing entry-containing cells is constructed [80] In our scenario, a 11x11 interpretative matrix is populated with elements from the logic knowledge base for cells having the value "1"
Step 8: The final phase is the development of the TISM model The information contained in the interpretive matrix is displayed on the nodes of the resultant diagram
Step 9: Calculation of Driving power and Dependence: The output of the TISM model is used as the input for the development of the DEMATEL cause-effect model After receiving all responses from experts in step 9, the average matrix (A) is calculated Subsequently, the first direct influence matrix D will be produced by normalizing the average matrix A so that all primary diagonal members equal zero On the basis of matrices D and E, the initial influence a factor sends to and receives from another is shown
Step 10: After normalizing the average decision matrix, the total influence matrix was derived based on the theory presented in the preceding section
Step 11: Set the threshold value to calculate the inner dependency matrix Next, the values of 'prominence' ('Di+Rj') and relation ('Di-Rj') were derived from the 'T' matrix Taking the mean of the 'T' matrix, the threshold value (a) was then calculated The values less than 'a' were removed from the 'T' matrix, and a new matrix, the inner dependency matrix was created
Step 12: Cause-effect relation map: based on the „D-R‟ values the challenges were categorized into cause-effect groups
Step 13: Integrating TISM – DEMATEL-based model: From the inner dependency matrix and the final TISM model, an integrated TISM-DEMATEL-based model was developed
Step 14: MICMAC is employed to identify the greatest barriers to blockchain adoption MICMAC provides a visual representation of the barriers in four quadrants based on the propulsion and dependency power of each barrier, which is determined by the total number of the final reachability matrix's rows and columns In the case of barrier B2, the row-wise sum for B2 in row 2 equals its propelling force, whereas the column- wise sum for B2 in column 2 equals its reliance
Master Thesis [40] Khúc Quang Trung - 2170307