Blockchain technology applications and challenges in the Saudi Arabian construction industry

The first implementation of BCT was Bitcoin (Blockchain 1.0), a widely accepted payment cryptocurrency. Bitcoin initially lacked programmability and focused on facilitating secure peer-to-peer financial transactions in a decentralised environment using public key encryption (Nakamoto 2008). Building upon Bitcoin’s foundation, Blockchain 2.0 introduced programmable smart contracts, aiming to disrupt the financial market. Blockchain 3.0 further expanded the technology’s scope by incorporating decentralised applications (DApps) and focusing on improvements in transaction speed, scalability and ease of implementation for industrial use (Nawari and Ravindran 2019). The latest iteration, Blockchain 4.0, offers significant potential when combined with artificial intelligence (AI), enabling systems to make autonomous decisions without human intervention. Li et al. (2022) suggested that the future holds even more possibilities with the emerging Blockchain 5.0. While these iterations highlight BCT’s evolving capabilities, they are all underpinned by a foundational architecture that defines its core functionality as a secure, decentralised and immutable system.

The framework of BCT functions as a distributed database system that ensures secure and transparent data storage. It differs from traditional databases in several key ways, including decentralised, immutable and transparent distributed ledgers (Choi 2019). It allows for direct peer-to-peer transactions that are validated through a securely designed system in which transactions are transparent and immutable, thereby removing the necessity for third-party trust. It is a decentralised network that operates on top of Internet protocols, recording transactions immutably through the use of cryptography and distributed consensus algorithms among a distributed set of users (Crosby et al. 2016). In a blockchain system, every transaction is recorded on a ledger and placed into a block linked to the preceding and following blocks. Once a block is added to the chain, it becomes immutable. The blockchain is verified through automation and governance protocols that cannot be altered or deleted by any single participant (Zheng et al. 2017; Six et al. 2022). It creates data blocks, starting with the ‘Genesis block’ which holds a set amount of encrypted transaction data. Each subsequent block references the previous block’s hash value, creating a chain where altering any block requires changing all preceding blocks, making it highly resistant to manipulation (Kim et al. 2020; Nanayakkara et al. 2021). These structural principles give rise to two distinct blockchain architectures: (1) permissionless system and (2) permissioned system that address different levels of accessibility, trust and governance needs.

Permissionless blockchains are open to anyone, allowing public transactions and consensus without requiring prior approval. Users can participate anonymously and are often incentivised to do so. By contrast, permissioned blockchains require invitations for users to join, and the network often restricts the types of users allowed to participate (Fernández-Caramés and Fraga-Lamas 2018; Li et al. 2022). While the construction industry is just starting to explore BCT’s potential, this technology offers significant data integrity and security advantages. Its collaborative nature allows participants to collaborate transparently on recording, verifying, storing and retrieving information (Kim et al. 2020). Additionally, research by Li et al. (2019) suggests that integrating blockchain with BIM can improve cybersecurity and optimise energy management within the construction supply chain. These demonstrated benefits highlight BCT’s core characteristics of transparency, security and decentralisation, making it a transformative tool across various industries.

The advantages of BCT are quite substantial, owing to its characteristics. Blockchain offers transparency (Li et al. 2022; Okanlawon et al. 2024). Utilising blockchain hashes, participants’ transaction records can be verified in real time and are immutable. Its collaborative nature allows participants to collaborate transparently on recording, verifying, storing and retrieving information (Kim et al. 2020). Furthermore, blockchain minimises reliance on third parties in decentralised peer-to-peer network transactions (Crosby et al. 2016; Choi 2019). Blockchain also enhances security by establishing a consensus of trust across the network, thereby making it challenging for hackers to infiltrate. Kouhizadeh et al. (2020) also emphasised that the data recorded in the database are permanent and resistant to manipulation.

BCT’s knowledge and awareness levels among construction professionals are critical factors influencing its adoption and implementation. The results of various studies have highlighted a general lack of awareness and understanding of BCT among construction professionals. For example, a qualitative study conducted by Reza (2020) interviewed experts and non-experts in BCT in the US construction industry. The study has shown that most participants did not understand BCT. According to Li et al. (2019), many professionals in the construction industry have limited knowledge of BCT concepts and their potential applications in construction projects. Azmi et al. (2022) used a mixed-methods research approach to assess the actors’ readiness to produce concrete goods, emphasising the necessity of adopting BCT in the Saudi Arabian construction sector. Their findings revealed a low level of BCT knowledge within Saudi Arabia’s construction industry, attributing this lack of knowledge to the fact that 90% of respondents had not worked with BCT. Mohammed and Alharthi (2022) highlighted this lack of knowledge within the Arab Region as well. A recent study by Gouda Mohamed et al. (2025) aimed to investigate the awareness and understanding of smart contracts among construction industry stakeholders in the Middle East and North Africa (MENA) region. They highlighted a significant lack of awareness, knowledge and practical implementation among these stakeholders. Table 1 summarises the knowledge and awareness context in the construction industry.

The technology acceptance model (TAM) and the technology–organisation–environment (TOE) model are among the most commonly used theoretical frameworks for explaining the barriers and drivers of BCT adoption in the construction industry. These models offer a foundation for understanding how perceptions of usefulness, ease of use and organisational dynamics shape the acceptance and implementation of this innovative technology. Remarkably, they highlight the importance of perceived benefits, top management support and organisational readiness in facilitating BCT integration, ultimately transforming traditional construction practices and enhancing overall efficiency (Graham 2019; Ershadi et al. 2021; Figueiredo et al. 2022; Li et al. 2022; Singh et al. 2023; Xu et al. 2023; Legesse et al. 2024). In construction, where adoption hinges on both individual stakeholder buy-in and organisational adaptability, TAM and TOE provide a robust theoretical foundation to unravel adoption complexities.

The TAM is a prominent framework for understanding the factors influencing user acceptance of new technologies. It suggests that perceived usefulness (PU) and perceived ease of use (PEOU) are critical drivers in adopting technological innovations (Wang et al. 2022). Meanwhile, the TOE model assumes that companies’ adoption of technological innovation is contingent on three main factors: the technological characteristics of the proposed innovation (e.g. BCT), organisational factors and environmental conditions. Studies conducted by Li et al. (2022) and Singh et al. (2023) confirmed the validity of this model within the construction industry worldwide.

Research has also shown that integrating the TAM with the TOE framework offers a comprehensive understanding of the factors influencing the acceptance of BCT in construction (Legesse et al. 2024). Similarly, a study by Li et al. (2022) examined a model that integrates the TOE model with market barriers to examine the barriers and factors in adopting BCT in the construction sector. Furthermore, a study by Shaharul et al. (2024) integrated the theory of planned behaviour (TPB) with the TOE model. It effectively evaluated the drivers of green building technology (GBT) adoption, proposing insightful research directions for advancing sustainability in the construction sector.

Other adoption models that have been used to evaluate the barriers and drivers of BCT adoption in various industries include the diffusion of innovation (DOI) model, the TPB, the theory of reasoned action (TRA) and the unified theory of acceptance and use of technology (UTAUT). Nevertheless, Legesse et al. (2024) demonstrated that these models have not been widely employed for BCT adoption in the construction sector. Figure 1 illustrates the theoretical framework of this study.

Fig. 1:

Theoretical framework of the study. BCT, blockchain technology.

To examine the barriers and drivers of BCT adoption in the Saudi construction industry, this study is primarily grounded in the TOE framework. This framework offers a structured lens for analysing how technological, organisational and environmental factors influence technology adoption at the firm level. In alignment with the aims of this study and to enhance the framework’s applicability to the context of BCT adoption in construction, this study extends the TOE model by incorporating additional dimensions related to human, knowledge and awareness factors. These additions help capture critical behavioural and cognitive aspects that are particularly relevant when assessing the adoption of complex and emerging technologies like BCT.

This study utilises a quantitative cross-sectional design. This approach requires quantitatively assessing the topic of evaluation and conducting systematic inquiries using tools such as opinion polls, questionnaires and surveys. Statistical analyses are then applied to the collected data to produce findings that contribute to the existing literature (Maksimović and Evtimov 2023). Quantitative research provides several benefits, including reaching a larger sample size, facilitating efficient data collection and enabling the generalisation of results (Sciberras and Dingli 2023). The research design involved administering a self-reported structured questionnaire survey to Saudi Arabian professionals in the construction industry.

The target population of this study consists of the teams of all active main contractors within the construction department of the Royal Commission of Ras-Alkhair, situated in the Eastern Province of Saudi Arabia, as well as all Ministry of Municipalities and Housing (MOMAH) registered first-and second-grade contractors. Thus, the population size is 107, and it is classified and officially registered as first-to second-grade by MOMAH and the Royal Commission.

The minimum required sample size is determined using the Krejcie and Morgan (1970) table. A population ratio of 50% is assumed to achieve the maximum sample size. A power of 80% is established, with a confidence interval of 95% and a margin of error of 5%. This study’s sample size is 84 participants.

The current study adopts a structured questionnaire to collect data. A comprehensive literature review identified the constructs of knowledge, barriers and drivers for BCT adoption in the construction sector. These three constructs were then formulated into a close-ended questionnaire used to collect data from respondents within the study’s scope. The questionnaire underwent a thorough review process by two experts in the field of construction management who are classified as Consultant Engineers by the Saudi Council of Engineers. The first expert is a project manager with 21 years of experience in ‘Master Planning and Engineering Management’. The second expert is the Head of a mechanical engineering department with 19 years of experience in ‘Project Management’. More details about these experts are provided in Table 4.

These experts evaluated the items’ clarity, relevance and comprehensiveness, ensuring they accurately reflected BCT adoption’s perceived barriers and drivers. Their feedback led to refinements in wording, phrasing and editing items to enhance the overall clarity and understanding for respondents. Furthermore, they suggested adding some new items or discarding other items for each latent construct. The knowledge construct begins with a brief definition of BCT, followed by a question asking respondents to rate their knowledge on a 5-point rating scale as follows: ‘1’– I have never heard about it; ‘2’ – I have heard about it; ‘3’ – I have limited experience in this field; ‘4’ – I have great experience in this field; and ‘5’ – I am an expert in this field (Chengyue et al. 2021). Then, the section introduces potential parameters to measure the application of BCT in the construction industry, which comprises 10 items measured on a 5-point Likert scale. The items measuring barriers and driver construction were also measured using a 5-point Likert scale. A copy of the study questionnaire is provided in the Supplementary materials and shows the source of each developed item.

The questionnaire was administered to collect data from the target population. An online version of the questionnaire was created using Jotform (https://www.jotform.com/), and a cover letter explained the study’s scope and objectives. The link to the questionnaire was sent to the study participants via online platforms, including official emails and WhatsApp.

Confirmatory factor analysis (CFA) was conducted to evaluate construct validity for each construct using AMOS software version 26.0. [Computer software]. Armonk, NY: IBM Corp. Construct validity was assessed through model fit indices, convergent validity and discriminant validity (Hair et al. 2019; Cheung et al. 2024). The goodness of fit of the latent constructs was assessed using several key fit indices. A chi-square minimum discrepancy divided by degrees of freedom (CMIN/DF) <5.0 indicates an acceptable fit (Kline 2016). Additionally, values >0.90 for the comparative fit index (CFI), goodness of fit index (GFI), normed fit index (NFI) and Tucker-Lewis index (TLI) suggest a better fit (Bentler 1990; Hu and Bentler 1999). Furthermore, a root mean square error of approximation (RMSEA) <0.08 indicates a reasonable fit (Browne and Cudeck 1992). Convergent validity was assessed using the standardised regression weights (factor loading) with a threshold point of ≥0.50, and an average variance extracted (AVE) exceeded the threshold value of 0.50, confirming acceptable construct convergence validity for each respective construct (Sekaran and Bougie 2016; Hair et al. 2019; Cheung et al. 2024). Furthermore, discriminant validity was assessed using the Fornell and Larcker criterion, which ensures that discriminant validity is achieved when the square root of the AVE exceeds the correlations between every two constraints in the same row and columns (Fornell and Larcker 1981; Cheung et al. 2024). Construct reliability was assessed using Cronbach’s alpha and composite reliability (CR). The values of Cronbach’s alpha and CR exceeded the threshold value of 0.70, indicating acceptable levels of internal consistency for each construct (Nunnally and Bernstein 1994; Hair et al. 2019; Cheung et al. 2024).

Figure 2 shows the results of CFA for the knowledge and awareness construct. All model indices passed their respective threshold points, indicating an acceptable level of construct validity for the knowledge and awareness latent construct. The values of factor loading ranged between 0.609 and 0.863, exceeding the cut-off value of 0.50. Furthermore, the value of the AVE was 0.529, which surpasses the cut-off value of 0.50. Therefore, the knowledge and awareness construct demonstrated good convergent validity. As for reliability indicators, the results showed that the values of Cronbach’s alpha and CR were 0.908 and 0.917, respectively, highlighting that construct reliability had been achieved.

Fig. 2:

CFA results for the knowledge and awareness of BCT. BCT, blockchain technology; CFA, confirmatory factor analysis; CFI, comparative fit index; GFI, goodness of fit index; NFI, normed fit index; RMSEA, root mean square error of approximation; TLI, Tucker-Lewis index.

Figure 3 presents the CFA results for the barriers in adopting BCT in the Saudi construction sector. It reports standardised regression weights (factor loadings) and model fit indices. The goodness-of-fit indices met all of the established cut-off criteria, confirming an acceptable level of construct validity.

Fig. 3:

CFA results for the barriers in adopting BCT in the Saudi construction sector. BCT, blockchain technology; CFA, confirmatory factor analysis; CFI, comparative fit index; GFI, goodness of fit index; NFI, normed fit index; RMSEA, root mean square error of approximation; TLI, Tucker-Lewis index.

Furthermore, Table A in the Appendix provides detailed information on each latent construct’s factor loadings, AVE, Cronbach’s alpha and CR. The results indicate that all standardised regression weights exceed the 0.5 cut-off value (Figure 2), and the AVE for each latent construct surpasses the 0.5 threshold point, establishing convergent validity. Furthermore, Table B in the Appendix presents the Fornell and Larcker matrix, which demonstrates that none of the correlation coefficients exceed the square root of the AVE in their respective rows and columns, confirming discriminant validity for this construct. Thus, the findings suggest that the barriers construct meets acceptable construct validity and reliability levels.

The study also assessed the validity and reliability of the construct for the drivers of BCT applications within the construction industry. Figure 4 shows the results of the CFA, indicating that all fitness indices met the best-fit criterion, providing evidence of acceptable construct validity. Moreover, the figure shows that the standardised regression weights exceeded the threshold of 0.50, confirming an adequate level of construct validity (Figure 3). Furthermore, the AVE values for all latent constructs were >0.50, further supporting the convergent validity of the drivers’ construct, as presented in Table C in the Appendix. Regarding discriminant validity, the Fornell-Larcker criterion revealed that none of the correlation coefficients exceeded the square root of the AVE for their respective rows and columns, as shown in Table D in the Appendix. As for construct reliability, the results displayed in Table C in the Appendix show that the CR and Cronbach’s alpha values for all subscales exceeded the threshold value of 0.70, indicating that construct reliability has been achieved.

Fig. 4:

CFA results for the drivers in adopting BCT in the Saudi construction sector. BCT, blockchain technology; CFA, confirmatory factor analysis; CFI, comparative fit index; GFI, goodness of fit index; NFI, normed fit index; RMSEA, root mean square error of approximation; TLI, Tucker-Lewis index.

Counts and percentages were used to describe the demographic and professional characteristics, and means and standard deviations were used to analyse the questionnaire items.

For the barriers and drivers construct, only responses to the first question regarding knowledge about BCT, which was answered with a score of ≥3, were analysed for these two subscales. All questionnaire items, scales and subscales were described as means and standard deviations.

Ethical approval was obtained from the research ethics committee at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia. Informed consent was obtained from participants before filling out the study questionnaire. Participation in this study was entirely voluntary.

A total of 84 participants responded to the study questionnaire. Table 5 displays the descriptive statistics of the respondents involved in the study. It reveals that 34.5% of all respondents were project managers, 25.0% were senior engineers, 16.7% were procurement managers, 11.9% were project directors and 8.3% were construction managers. Meanwhile, a significantly low proportion of them held positions such as production manager, contracting manager or assistant head (1.2%). Table 5 shows that a significant proportion of respondents had a Bachelor’s degree (83.3%), while 14.3% had Master’s degrees and 2.4% had diploma degrees. Moreover, the respondents’ professional experience varied and was categorised into multiple levels. A significant proportion of them had 10 years to <15 years (52.4%), while 32.1% had 5 years to <10 years, 8.3% had 15 years to <20 years and 3.6% had <5 years. However, none of them fell within the 20–25 years of experience category. Furthermore, the majority of participants have been involved in at least two types of construction projects (77.4%). By contrast, 10.7% have been involved in building projects only, 4.8% in utility projects, 4.8% in special structures, 1.2 in industrial and 1.2% in infrastructure projects only. Additionally, a significant proportion of respondents were engaged in projects for which the type of contracts were lump sum contracts and unit cost contracts (57.1%). In comparison, 29.8% were engaged in unit cost contracts only, and 10.7% were engaged in lump-sum contracts only. However, a very small proportion of them were engaged in several contract types (2.4%).

When participants were asked about their roles in the projects they participated in, 13.0% of them reported being project managers, 7.1% were project engineers, 6.0% were quality controllers and 6.0% were procurement managers. Meanwhile, 50.0% of participants reported having a combination of two roles in these projects, while 17.9% participated in a combination of three roles. When participants were asked about the average project size (in million Saudi Riyals (SAR)) in participants’ companies, the findings reveal that 45.2% of respondents participated in projects with an average size of 50 million to <100 million SAR, while 32.1% participated in projects ranging from 100 million to <150 million SAR. Additionally, 13.1% participated in projects with <50 million SAR, 4.8% in projects ranging from 150 million to <200 million SAR, 3.6% in projects of 250 million SAR or more and 1.2% in projects ranging from 200 million to <250 million SAR (Table 5).

The knowledge construct begins with a question asking respondents to rate their knowledge of BCT in general. Figure 5 shows that most respondents reported having limited experience with BCT (81.0%), while 3.6% (n = 3) claimed to have significant experience. However, 10.7% of respondents reported having heard about BCT, and 4.8% stated that they had not heard about it at all. Nonetheless, none of them reported being experts in this field.

Fig. 5:

Participants’ general knowledge of BCT. BCT, blockchain technology.

The average composite score of the knowledge and awareness construct is 3.26 (SD = 0.67), indicating that participants exhibited moderate levels of knowledge and awareness about BCT in the construction industry. In terms of percentage, about 65.2% of respondents exhibited moderate knowledge and awareness about BCT in the construction industry. The highest average on knowledge and awareness items was found in the following item, ‘I believe that increased knowledge of BCT could positively impact the efficiency of construction project management’, with a mean of 3.67 (SD = 0.83), while the item ‘I have received formal training or education on BCT in the context of construction projects’ scored the lowest average knowledge and awareness (mean = 2.63, SD = 1.21), as shown in Table 6.

Therefore, the current study revealed that participants had moderate knowledge of BCT in the construction industry and that not all of them possessed sufficient knowledge of this technology, which is consistent with some previous studies. For example, Azmi et al. (2022) reported that 76% of respondents reported having either no, limited or very limited comprehension of BCT, while only 23.3% demonstrated good or satisfactory familiarity with BCT within the Saudi Arabian construction industry. A study by Reza (2020), which interviewed experts in the construction industry in the United States, indicated that most participants exhibited a lack of understanding about BCT. Another study conducted by Mohammed and Alharthi (2022) indicated that construction professionals in the Arab region lack sufficient knowledge of the potential benefits of adopting BCT in the construction industry. However, a study conducted by Kim et al. (2020) indicated that procurement management and cost management were among the areas with the highest level of knowledge regarding implementing BCT in the construction sector.

This section presents the results of participants’ reported responses on the barriers to implementing BCT in the Saudi Arabian construction sector. To ensure the reliability of the findings, the analysis in this section is based on participants’ perceived barriers, excluding those who exhibited a knowledge score of <3 and those who reported no knowledge of BCT. Therefore, 13 responses were discarded from the analysis, resulting in a final sample size of 71 participants (see Figure 5).

Table 7 and Figure 6 present the means and standard deviations of all items underlying each latent construct, along with the composite score of each subscale. The findings revealed that the highest perceived barriers to implementing BCT in the Saudi Arabian construction sector were related to human factors (mean = 3.93, SD = 0.37), followed by industrial (mean = 3.88, SD = 0.40), technical (mean = 3.86, SD = 0.34), organisational (mean = 3.76, SD = 0.43) and legal (mean = 3.74, SD = 0.44) barriers.

Fig. 6:

Mean scores and error bars for the subscales of the barriers to implementing BCT in the Saudi Arabian construction industry. BCT, blockchain technology.

More specifically, the highest mean score for human barriers was found in the item ‘Fostering a culture of innovation and continuous learning within the organisation’, with a mean score of 4.11 and a standard deviation of 0.73. Conversely, the lowest mean score was found in the following item: ‘Having low trust towards new technologies like blockchain’, with a mean score of 3.62 and a standard deviation of 0.83, as shown in Table 7. This finding is congruent with Weerakoon and Chandanie (2021), who highlighted that the lack of technical proficiency among professionals in the construction industry serves as a major obstacle in adopting new technologies such as BCT. Similarly, Anuradha et al. (2023) indicated that the lack of BCT experts in the construction industry and limited technological proficiency among industry and IT-related employees were among the most significant human barriers to implementing BCT. This suggests that enhancing industry professionals’ knowledge and technical expertise regarding blockchain is essential for effectively implementing BCT and smart contracts within the construction industry.

Concerning the industrial barriers subscale, the item ‘Establishing industry standards or guidelines for implementing BCT’ scored the highest mean score (mean = 4.01, SD = 0.78), while the item ‘Competing effectively with other construction firms that have not yet adopted BCT’ scored the lowest mean score (mean = 3.80, SD = 0.79) as displayed in Table 7. This finding is congruent with Anuradha et al. (2023), who showed that the slow adoption of new technologies and the persistence of prevailing industry practices, such as conventional requirements and norms, pose obstacles to innovation and technological advancement within the industry.

As for the technical barriers subscale, the item ‘Overcoming the limited availability of technical expertise and resources for developing and deploying blockchain applications’ scored the highest mean score (mean = 4.15, SD = 0.65). By contrast, the lowest mean score was found in the item ‘Having insufficient connectivity and bandwidth’, with a mean score of 3.48 and a standard deviation of 0.73, as indicated in Table 7. Our results are comparable with several previous findings (Kim et al. 2020; Anuradha et al. 2023; Singh et al. 2023; Okanlawon et al. 2024; Gouda Mohamed et al. 2025).

Regarding the organisational barriers subscale, the highest mean score was found in the item ‘Shifting organisational culture to prioritise innovation and technological advancements, including blockchain in the construction industry’, with a mean score of 3.97 and a standard deviation of 0.76. By contrast, the lowest mean score of 3.52 (SD = 0.86) was found in the item ‘The reluctance of companies to invest in new technologies’ (Table 7). The current study revealed similar to those results reported in the literature (Kim et al. 2020; Teisserenc and Sepasgozar 2021; Wang et al. 2022; Singh et al. 2023; Okanlawon et al. 2024).

As for the legal barriers subscale, the item ‘Navigating uncertainty about regulatory frameworks and compliance requirements for BCT’ scored the highest average of 3.85 (SD = 0.67). By contrast, the item ‘Absence of established legal protocols to deter misconduct’ scored the lowest average of 3.63 (SD = 0.80), as displayed in Table 7. These results are comparable with barriers related to legal and data privacy reported in previous studies, which emphasised the importance of implementing adequate safety measures, establishing clear regulatory frameworks and enforcing robust data privacy protocols to ensure the effective implementation of BCT, as well as the lack of government policies and protocols to handle misconduct (Boonpheng et al. 2020; Teisserenc and Sepasgozar 2021; Anuradha et al. 2023; Singh et al. 2023; Xu et al. 2023; Waqar et al. 2024).

Therefore, this construct yields favourable outcomes in identifying the most significant hurdles impacting the adoption of BCT in construction projects in Saudi Arabia. The identified human, industrial, legal, technical and organisational barriers are congruent with several previous findings (Turk and Klinc 2017; Graham 2019; Li et al. 2019; Boonpheng et al. 2020; Wang et al. 2020; Hamledari and Fischer 2021; Teisserenc and Sepasgozar 2021; Mohammed and Alharthi 2022; Anuradha et al. 2023; Singh et al. 2023; Xu et al. 2023; Waqar et al. 2024). For example, a study conducted by Waqar et al. (2024) investigated the obstacles facing the application of BCT in Malaysian small-scale construction projects. They identified several barriers, including economic and planning barriers, privacy and regulation, operational barriers, technological barriers and work environment barriers. Another study conducted by Teisserenc and Sepasgozar (2021) proposed a potential framework for adopting BCT in the construction industry based on several barriers. These barriers include blockchain performance, digital twin, economic, environmental, legal, political, security, smart contracts, and social and technological barriers. Studies conducted in Saudi Arabia have highlighted significant technological barriers, such as a lack of skilled personnel and resources for developing and deploying BCT applications within existing systems (Mohammed et al. 2021). In Europe, however, previous research has highlighted that technological maturity and infrastructure constraints are among the most prominent barriers in adopting BCT in the construction sector (Wu et al. 2022). In the Arab region, a study by Mohammed and Alharthi (2022) indicated various obstacles to the application of BCT in the construction sector, including BCT understanding, complexity, initial cost, privacy and security and storage problems. In line with our study findings, Graham (2019) and Anuradha et al. (2023) identified several barriers to implementing BCT in the construction section, including human, industrial, legal, political, technical and organisational barriers. However, Anuradha et al. (2023) identified the technical barriers, including the scarcity of construction-related software applications utilising BCT, as one of the most significant impactful barriers, which is inconsistent with our findings regarding the rank of these barriers. Moreover, Singh et al. (2023) identified significant obstacles in adopting BCT in sustainable construction projects, including challenges with implementing blockchain-based policies and customer unawareness and resistance to BCT. Additionally, they highlighted technology immaturity, market uncertainty and competition, and limited technology accessibility as crucial obstacles.

This section presents the results of participants’ responses on the perceived key drivers of BCT applications in the Saudi Arabian construction industry. The analysis of this section is based on only 71 responses, similar to the previous section.

Table 8 shows the mean and standard deviations of all items measuring each subscale and the summary statistics of the composite score of each subscale. The levels of agreement for all items of each subscale exceeded 50.0% point (3 out of 5 points), indicating higher levels of agreement with the items measuring the dimensions of the proposed framework for the application of BCT in the construction industry in Saudi Arabia. The findings indicated that the most important scales for the key drivers of BCT applications within the construction industry were smart contracts (mean = 3.99, SD = 0.54) and transparency and traceability (mean = 3.85, SD = 0.34). This is followed by innovation potential (mean = 3.83, SD = 0.34), regulatory compliance (mean = 3.78, SD = 0.43), data security (mean = 3.75, SD = 0.44), decentralisation (mean = 3.74, SD = 0.53) and stakeholder engagement (mean = 3.74, SD = 0.38) dimensions. Meanwhile, the least perceived scales for implementing BCT in the Saudi Arabian construction industry were cost saving (mean = 3.72, SD = 0.51) and interoperability (mean = 3.63, SD = 0.50) dimensions. Figure 7 illustrates these findings.

Fig. 7:

Mean scores and error bars for the subscales of the key drivers of BCT applications within the construction industry. BCT, blockchain technology.

The current findings emphasise the importance of various factors in adopting BCT within construction, particularly emphasising smart contracts, transparency and traceability, which are comparable with several previous findings. Kamel et al. (2023) developed a framework for smart contracts in construction, exploring payment automation scenarios and showcasing the effectiveness of integrating different methods for managing smart contracts. Hultgren and Pajala (2018) highlighted BCT’s potential to enhance transparency and traceability across the construction supply chain. Kim et al. (2020) identified key areas for BCT application in construction, focusing on Project Cost/Change Management, Contract Bidding and Formation, and Procurement Evaluation. Teisserenc and Sepasgozar (2021) proposed a comprehensive conceptual framework for BCT adoption in the Building, Engineering, Construction, Operations, and Mining (BECOM) industry 4.0, addressing technological factors and integrating BCT with IoT, BIM and digital twins (DT). Olawumi et al. (2021) emphasised stakeholder awareness, top management support and standardised BCT solutions as crucial for adoption. Cheng and Chong (2022) found that institutional pressures, functional benefits and behavioural control promote BCT adoption in China. Chengyue et al. (2021) highlighted factors like PU, ease of use, knowledge, risk and trust affecting BCT adoption in complex industrial systems. Wang et al. (2022) and Li et al. (2022) identified technical, organisational and environmental factors influencing BCT adoption in construction, with competitive pressure being the most influential.

The study aims to develop a comprehensive framework for BCT implementation in the Saudi Arabian construction industry to benefit stakeholders, industrial decisionmaking and the government. The framework draws from the existing literature and insights gathered through a questionnaire survey of construction professionals in the Eastern region of Saudi Arabia.

Figure 8 illustrates the various stages of the proposed framework’s development. First, it utilises the existing literature to develop a questionnaire survey assessing knowledge, barriers and key drivers for BCT implementation. Second, the framework assesses participants’ knowledge levels to identify potential deficiencies and suggests strategies for improvement. Third, it identifies and ranks human, industrial, technical, organisational and legal barriers and proposes strategies to overcome them.

Fig. 8:

A comprehensive proposed framework for implementing BCT in the Saudi construction sector. BCT, blockchain technology.Research implications and limitations

Next, the framework identifies key BCT applications’ potential drivers, ranking applicable areas such as smart contracts, transparency, innovation potential, regulatory compliance, data security and cost-saving. It also outlines the potential benefits and outcomes of adopting BCT in the Saudi Arabian construction industry.

To validate the framework, experts in the field review it. They generally agree with its proposals but suggest modifications for clarity and relevance. The framework is evaluated using a 5-point Likert scale, with experts providing feedback to enhance its comprehensiveness and applicability. Table E in the Appendix shows the detailed validation procedure.

While the framework has not yet been validated for its applicability, it provides a systematic process for enhancing knowledge, identifying barriers and understanding key drivers of BCT application in the Saudi Arabian construction sector. Future research could validate and assess its applicability to further enhance evidence-based action in the industry.

The significant implications of this study are important for policy considerations. As the Saudi Arabian construction sector undergoes digital transformation, policymakers must proactively address the challenges and opportunities associated with BCT adoption. The findings suggest that the Saudi construction industry is in the early stages of BCT adoption, hindered by limited knowledge and key barriers. However, growing interest in BCT’s benefits, such as transparency and cost-saving, highlights its potential. Targeted policies, training and infrastructure investment are essential to enhance digital maturity and adoption.

For example, there is a pressing need to develop and implement educational programmes and training sessions aimed at enhancing professionals’ knowledge about BCT and its applications within the Saudi Arabian construction industry. Such initiatives would require collaborative efforts among educational institutions, industry associations and government bodies to ensure comprehensive coverage and accessibility of training resources. This can be achieved by integrating blockchain-related modules into university curricula, offering specialised certification programmes and conducting industry-wide awareness campaigns to bridge the existing knowledge gap.

Additionally, the study underscores the necessity of establishing a regulatory framework that addresses the human, industrial, technical, organisational and legal barriers identified herein. This framework should offer guidance and standards for implementing BCT in construction projects, ensuring compliance with legal requirements and industry best practices. A well-defined regulatory framework can help mitigate uncertainties surrounding data privacy, intellectual property rights and contractual obligations within blockchain-enabled construction processes. Policymakers should collaborate with legal experts, industry leaders and technology developers to draft clear and enforceable policies that facilitate BCT adoption while safeguarding stakeholder interests.

Furthermore, the study advocates for introducing incentive programmes to stimulate BCT adoption within the construction sector. Financial incentives, tax breaks or other benefits could be offered to companies demonstrating successful integration of BCT into their project management practices and showcasing tangible improvements in efficiency, transparency and cost-effectiveness. Governments could also establish innovation grants and funding schemes for startups and enterprises developing blockchain solutions tailored to the construction industry. Public-sector pilot projects can also serve as demonstration models, showcasing the advantages of blockchain integration and encouraging wider adoption across private-sector construction firms.

Finally, allocating funding for research and development initiatives to explore BCT’s potential applications in enhancing project management practices is deemed imperative. This would involve supporting academic research projects, industry collaborations and pilot studies to validate the effectiveness of BCT solutions in real-world construction scenarios. Government agencies should support interdisciplinary research on the economic, technical and social impacts of BCT in construction. Research partnerships between universities and firms can drive innovation and practical implementation in large projects.

This study provides significant theoretical contributions by extending the traditional TOE framework to include human, knowledge and awareness-related factors. While the classical TOE framework offers a structured lens to examine technology adoption through technological, organisational and environmental contexts, it often overlooks the human-centric elements that are crucial for understanding adoption behaviour in complex sectors like construction.

This study also highlights that human-related barriers, like lack of awareness, knowledge and training, and innovation culture, are more critical than technical or legal issues in the Saudi construction industry. This emphasises the need to incorporate the human dimension more strongly into frameworks like the TOE model. By systematically mapping barriers (e.g. regulatory uncertainty, cultural resistance) and drivers (e.g. smart contracts, stakeholder engagement) to TOE’s triple structure along with human-related barriers, the study offers a structured roadmap for future research on innovation adoption in similar sectors. The current study aligns closely with Li et al. (2022) and Singh et al. (2023), who validated the efficacy of the TOE theory within the construction industry.

Therefore, the revised TOE framework not only better captures the multidimensional nature of BCT adoption in the Saudi construction industry but also serves as a foundation for future research exploring the intersection of human capital, organisational learning and innovation diffusion.

Limitations of the study include its focus on construction companies with significant public unit price contracts solely in the Eastern Province of Saudi Arabia, thereby restricting the scope to this specific region and potentially excluding projects and contractors from other areas. Moreover, the targeted participants comprised construction professionals exclusively from projects within the Eastern region, possibly limiting the generalisability of the results to the broader construction industry context. Additionally, the proposed framework for BCT adoption in the Saudi Arabian construction sector lacked validation from real-time projects, thus potentially limiting its validity and applicability in practical settings. Furthermore, the study’s reliance on research papers available at the time and input from construction and BCT experts underscore the limitations in available information and resources concerning BCT application in construction. As this field is still emerging, the findings are constrained by the existing literature and expert opinions available during the study period.

The construction sector in Saudi Arabia has witnessed significant growth. It is now recognised as one of the top-performing construction markets globally, holding a prominent position as one of the nation’s largest industries. It encompasses numerous ongoing projects with substantial total project value. With the rapid evolution of Industry 4.0 and the increasing integration of high technology, there is a growing interest in adopting BCT to enhance project management, efficiency and transparency.

This study highlights critical barriers hindering BCT adoption, including a widespread lack of professional knowledge in addition to human, industrial, technical, organisational and legal challenges. The findings reveal that while BCT has the potential to improve process performance and project oversight, there is still limited awareness and understanding of its applications among construction professionals in Saudi Arabia. Addressing these barriers requires targeted strategies, including industry-wide educational programmes, regulatory advancements, and investments in blockchain-compatible infrastructure.

Despite these challenges, the study identifies key drivers to accelerate BCT adoption in the Saudi construction sector. These include the potential of smart contracts, improved transparency and traceability, enhanced regulatory compliance, increased data security, decentralisation, stakeholder engagement, cost-saving benefits and interoperability. However, concerns regarding interoperability and integration with existing construction management systems remain, necessitating further exploration and technical advancements.

This study proposes a comprehensive framework for BCT implementation to bridge the knowledge gap and mitigate the barriers to adoption. The framework provides a structured approach to overcoming industry challenges while leveraging BCT’s benefits to enhance project performance and management. However, its practical applicability requires validation through future research efforts, particularly in real-world construction projects within Saudi Arabia. By refining and implementing this framework, industry stakeholders and governmental bodies can drive evidence-based decision-making and policy development to support BCT adoption.

The findings of this study emphasise the need for close collaboration among policymakers, construction stakeholders and private entities to facilitate BCT integration in the Saudi Arabian construction sector. Strategic efforts should address existing barriers while harnessing the identified drivers to create a more innovative, transparent and technologically advanced construction industry.

Future research could explore effective strategies for enhancing professionals’ knowledge of BCT and its applications in construction. This may involve investigating various training methods, educational resources and knowledge-sharing platforms to improve awareness and understanding among industry stakeholders. Further inquiry is necessary to examine strategies for overcoming identified barriers to BCT implementation in the Saudi Arabian construction industry. This could include analysing specific barriers such as technical limitations, organisational resistance or legal constraints and developing targeted interventions. Additionally, comprehensive assessments of BCT adoption potential and applicability in construction projects are needed. This could entail conducting case studies, surveys or experimental studies to evaluate the feasibility, benefits and barriers of implementing BCT solutions such as smart contracts and transparency mechanisms. Framework validation of the proposed comprehensive framework for BCT implementation in Saudi Arabian construction is essential, involving pilot implementations and stakeholder consultations to ensure alignment with industry needs and priorities.