1. Introduction
Blockchain is a hotly emerging digital technology and is considered as the fourth generation of the subversive core technological revolution after steam engines, electricity, and the Internet. Blockchain is a combination of a series of concepts. It is essentially a distributed database, which is decentralized, immutable, traceable, transparent, and reliable [1,2]. Along with the continuous development of blockchain, its application potential has received increasing attention from many countries. In January 2016, the British government released Distributed Ledger Technology: Beyond Blockchain, which is the white paper of blockchain special research report. Nasdaq took the lead in launching the equity trading platform “Nasdaq Linq” based on blockchain in December 2015. Japan established the Blockchain Alliance in April 2016. In China, a series of policy documents have been issued to promote the application of blockchain. Among them, in the relevant policies to promote the application of blockchain in the construction industry, it is proposed to increase the integration and innovative application of new technologies including blockchain in the whole construction process.
The construction industry is a highly decentralized industry, and the lack of a systematic and effective trust system, information sharing, and automation of the process is a problem in traditional construction project management [3]. Emerging digital technologies can help the construction industry integrate fragmented knowledge and information to improve organizational collaboration and communication [4]. In recent years, the construction industry has actively absorbed and applied emerging technologies to solve problems existing in its own development, thereby improving the level of informatization and digitization of the industry. However, with the trend of large-scale and complex engineering projects in the industry and the interaction of various information technologies, data query access is complex, data information is easily tampered with, and storage is too centralized; these and other problems are becoming increasingly obvious. The features of blockchain are a potential tool to solve the problems in the development of the construction industry [5]. Many researchers have recognized the potential role of blockchain in driving the development of the construction industry [6,7], but its practical application is very low. At present, the research on blockchain in the construction industry is mainly focused on exploring application scenarios and mining the application value of blockchain, and rarely research on the adoption mechanism of blockchain in the construction industry.
Blockchain is still a new concept for the construction industry, which has not fully realized the application value of blockchain and how to implement it, in addition to there being hesitation in its acceptance. Various difficulties still exist in applying blockchain technology to the construction industry. Therefore, it is necessary to further clarify the influencing factors and mechanisms of such adoption and promote construction industry adoption of blockchain at a fundamental level. Promoting the absorption and application of blockchain in the construction industry is of great significance to the development of the construction industry. In recent years, the technology acceptance model (TAM) has been widely used to understand the acceptance behavior of information technology. These theoretical models can understand the user’s adoption behavior of emerging technologies from the aspects of user attitudes, technical characteristics, and social impact and provide a reliable theoretical support for understanding the influent mechanism of the construction industry on the adoption of blockchain. Hence, the main research objectives of this paper are: (1) sort out the relevant theories of technology adoption to provide theoretical support for building a blockchain adoption model in the construction industry; (2) determine the factors that affect the adoption of blockchain in the construction industry, and analyze their impact on blockchain adoption behavior through empirical investigation.
2. Theoretical Framework and Research Hypothesis
TAM has provided effective research methods in previous research on innovative technology adoption. This study will sort out the relevant theories and research on technology adoption. Then, according to the characteristics of blockchain and the construction industry, the factors which affect the adoption of blockchain in the construction industry are determined and corresponding assumptions are made. In addition, this study will integrate the influencing factors and TAM with the help of the technology–organization–environment (TOE) framework to complete the blockchain adoption model that is suitable for the construction industry.
2.1. Theoretical Framework
In this study, the integrated model of TAM, particularity of construction industry, and TOE framework will be used to understand the acceptance of blockchain in the construction industry. Using structural equations, the proposed model of the construction industry’s intention of adopting blockchain will be examined through the survey of industry practitioners.
2.1.1. Technology Acceptance Model
TAM is a conceptual model proposed by Davis [8] in 1989 based on the theory of rational behavior (TRA) to study the user’s acceptance of information systems and was initially proposed to explain the decisive factors of widespread acceptance of computers. The TAM is shown in Figure 1. TAM has been continuously revised and expanded by the academic community since it was proposed. Davis et al. [9] added a series of external variables such as work relevance, output quality, and result presentation to the TAM, forming an extended technology acceptance model (TAM2). Venkatesh et al. [10] integrates multiple models such as task technology matching model, rational behavior theory, and planned behavior theory, and proposes the integration theory of technology acceptance and utilization (UTAUT). This led to TAM3, which contains all variables of the above model [11]. The TAM and its extended model are widely used to explain user acceptance of information technology.
2.1.2. Technology–Organization–Environment Framework
The TOE framework is an effective theory for testing the adoption of new technologies at the organizational level, with strong explanatory power. Most of the current IT adoption theories at the organizational level are other forms of the TOE framework [12]. The TOE framework summarizes the factors affecting the implementation of technological innovation by an enterprise or organization into three categories: technical, organizational, and environmental, which can categorize factors to integrate the internal and external factors in the construction industry’s adoption decisions. The classical TAM provides a methodology for interpreting individual behavior towards information technology adoption, while setting up external variables makes the model more flexible. The TOE framework comprehensively considers the internal and external influencing factors of the system from an organizational perspective and can effectively analyze the influencing factors from multiple perspectives. The combination of the two not only enriches the completeness of the investigation of the actors of information technology adoption, but also makes the adoption model structure more complete. In view of the advantages of the TOE framework, scholars have used it in different research contexts to determine the factors used by blockchain adoption [13,14,15].
2.2. Research Model and Hypothesis Development
In studies related to the use of TAM, the influence of the variable of attitude was not significant [8]. Referring to previous research, this paper also removes the variable of use attitude in TAM. There are undefined external variables in TAM, and there is no explicit pattern in the selection of external variables, giving the model structure a degree of flexibility. Adding external variables to TAM, or combining TAM with behavioral theories related to the adoption of other technologies, can improve the specificity and interpretive power of studies, and better explain the behavioral phenomenon of user acceptance of new technologies [16]. This study combines the TOE framework and characteristics of blockchain applications in the construction industry to study the factors affecting the perceived usefulness and perceived ease of use of blockchain in the construction industry. This allows for the design of a research model with the intention of the construction industry adopting blockchain, as shown in Figure 2, where +/− represents a positive/negative influence relationship.
2.2.1. Internal Variables of TAM
According to Davis et al. [8] perceived usefulness and perceived ease of use directly affect the adoption of technology. Perceived usefulness refers to the degree to which individuals believe that using a system can improve their job performance, and perceived ease of use refers to the degree to which an individual perceives the use of a system as effortless. Willingness to adopt refers to the degree of willingness of individuals to adopt a new technology when confronted with it [8]. In different research contexts, scholars have used TAM to study the intention of adopting blockchain. Kamble et al. [17] conducted a research survey on blockchain adoption behavior in the Indian supply chain using TAM, and found that perceived usefulness has an impact on the willingness to implement blockchain, and perceived ease of use positively affects perceived usefulness. Ullah et al. [18] found that in blockchain adoption behavior in the energy sector, perceived ease of use positively affects perceived usefulness, and perceived usefulness positively affects the energy sector’s behavioral intent towards blockchain. Other scholars have also demonstrated in different research contexts that perceptual usefulness and perceptual ease of use have a significant impact on the behavioral intent of technology adoption [19,20,21]. Therefore, this study proposes the following hypotheses:
Perceived usefulness has a positive effect on the construction industry’s intention of adopting blockchain.
Perceived ease of use has a positive effect on the construction industry’s intention of adopting blockchain.
Perceived ease of use has a positive effect on perceived usefulness.
2.2.2. Technical Dimension Expansion
(1) Relative advantage. Relative advantage can be reflected in the efficiency and economic benefits of the organization when using technology, as well as the competitiveness and reputation of the organization [22]. According to diffusion of innovations theory, the possibility of technology adoption and transmission speed are positively correlated with the prominence of relative advantage. Wong et al. [23] have shown that relative advantage has a significant impact on the behavioral intention of blockchain adoption in the research on the impact of Malaysian small- and medium-sized enterprises using blockchain for operation and supply chain management. The findings of other scholars have also shown that relative advantage can significantly affect perceived usefulness [21]. However, are these relative advantages of blockchain useful for the construction industry? This requires further exploration. Therefore, this study proposes the following hypothesis:
The relative advantage of blockchain has a positive effect on perceived usefulness.
(2) Compatibility. Compatibility refers to the extent to which innovation is consistent with socio-cultural values, past perspectives, and perceived needs [24]. The compatibility of blockchain should also include interoperability, that is, the ability to seamlessly exchange data between different blockchain applications and other software [25]. The early adoption of blockchain models poses risks to the incompatibility and interoperability of certain blockchains [26]. Li et al. consider the current interoperability as one of the obstacles to the full implementation of the smart contract function of blockchain in the construction industry [1]. The application of blockchain to the construction industry is bound to interact with the current working mode and the equipment and software used in the industry. Can the current workflow and software equipment in the construction industry match the blockchain? We also need to further analyze how the compatibility of the blockchain affects its ease of use. Therefore, this study proposes the following hypothesis:
Compatibility has a positive effect on perceived ease of use.
(3) Perceived cost of adoption. Adoption cost is a factor to be considered when introducing new technologies. The initial investment is required for the introduction of new technology, including the adoption cost of technology and the learning cost of being familiar with the system [27]. Rind et al. found that cost is an obstacle to the proliferation of large-scale mobile commerce in Pakistan [28]. Zainab et al. found that perceived cost has a significant impact on the adoption of e-training [29]. Owing to the novelty and complexity of blockchain, the initial cost of adopting it is relatively high. It is also necessary to develop proprietary solutions and appropriate professionals. The adoption of blockchain solutions may exceed the financial and personnel reserves of small- and medium-sized enterprises [30]. The adoption of blockchain may not produce certain benefits in the short term, which brings certain risks to the construction industry. Will the construction industry think blockchain adoption is too costly? Could this perceived cost of adoption make the construction industry think blockchain is useless? Therefore, this study proposes the following research hypotheses:
The perceived cost of adoption of blockchain has a positive effect on perceived usefulness.
(4) Technological maturity. The maturity of technology development is related to adopters’ views. When the technology is in the immature stage, there are often many problems to be solved in its implementation; that is, the adopter may think that the technology is difficult to use. As an emerging digital technology, blockchain itself still has many defects, such as the 51% attack problem, code vulnerability, and storage capacity limitation [5]. Ye et al. [31] found that the initial maturity of BIM technology is a key factor affecting BIM technology diffusion. The immaturity of new technologies hinders large-scale adoption, whereas most current studies show that the application of blockchain is still in the development stage [27]. The immaturity of blockchain and the imperfection of industrial support have created obstacles for the construction industry to adopt it [26]. The construction industry is a traditional industry, and the application of information technology often lags behind other industries. We need to verify whether the current blockchain products in the construction industry are mature enough, and whether the current technological maturity of the blockchain has an impact on ease of use. Therefore, this study proposes the following hypothesis:
The technological maturity of blockchain has a positive effect on perceived ease of use.
2.2.3. Organizational Dimension Expansion
(1) Organizational readiness. Organizational readiness refers to the available resources within the organization for adopting technological innovation, including existing infrastructure, relevant talents, and funds available for technology adoption [32]. Organizational readiness is also described as an organization’s absorptive capacity or ability to take advantage of innovations and existing knowledge [27]. Low et al. in the study of enterprises’ cloud computing adoption behavior, found that the technical readiness of organizations has a positive impact on enterprises’ cloud computing adoption [33]. Generally, the more resources an organization can provide for the adoption of new technologies, the easier it will be to adopt and apply new technologies, and the more it can give full play to the application value of new technologies. It is unclear whether the construction industry has the necessary readiness to adopt blockchain, and whether organizational readiness will change the construction industry beyond the application value of blockchain. Therefore, this study proposes the following hypothesis:
Organizational readiness has a positive effect on perceived usefulness.
Organizational readiness has a positive effect on perceived ease of use.
2.2.4. Environment Dimension Extension
(1) Competitive pressure. Competitive pressure refers to the degree of pressure on an organization caused by competitors seeking to gain more resources in a competitive market environment [34]. Competitive pressure often prompts organizations to adopt innovation and further optimize the allocation of production factors to ensure that they are not eliminated by the market [35]. Oliveira et al. found that the competitive pressure within the industry is a significant factor promoting e-commerce adoption [36]. Low et al. found that competitive and trading partner pressure had a significant impact on the adoption of cloud computing [33]. Other technology adoption studies have also confirmed that competitive pressure has a significant impact on the behavioral intention of new technology adoption [23]. As an emerging technology, it is unclear whether the construction industry agrees with the competitive pressure caused by its adoption. Therefore, this study proposes the following hypothesis:
Competitive pressure has a positive effect on perceived usefulness.
(2) Policy. Policies, norms, and guidance issued by the government will affect people’s perception of the difficulty and application value of emerging technologies [37]. Orji et al. [38] concluded that government policy support is one of the three important factors affecting the adoption of blockchain in the freight logistics industry. Suwanposri et al. [39] found that the support of government policies and regulations is an important environmental factor affecting the adoption of blockchain. Qin’s [40] research shows that the government can influence enterprises’ perceptions of BIM usefulness and ease of use through incentive policies. At present, compared with other technologies such as BIM, policies and specific application specifications to promote the adoption of the construction industry are scarce. Therefore, this study proposes the following hypothesis:
The completeness of policy has a positive effect on perceived usefulness.
The completeness of policy has a positive effect on perceived ease of use.
3. Methodology
3.1. Survey Design
In this study, questionnaires were used to collect data, and structural equations were used to conduct empirical research. Based on the existing literature and measurement scales combined with the characteristics of blockchain use in the construction industry, a preliminary questionnaire was designed to carry out a small-scale pilot test with industry experts and middle and senior management of construction enterprises. Based on the test comments, the questionnaire was adjusted and revised to form the final questionnaire, as shown in Table 1. Before distributing the questionnaire to organizations related to the construction industry and blockchain companies, we explained the blockchain technology and gave some examples of application scenarios.
3.2. Data Collection
A questionnaire survey was carried out among the construction industry practitioners, and 368 questionnaires were recovered; 256 valid questionnaires were obtained at an effective rate of 69.6%. The data are presented in Table 2. Males accounted for 69.1% of the total, which is in line with the actual situation of the construction industry. The results are presented in Table 2. A total of 87.1% held a bachelor’s degree or above, and 66.8% held managerial positions. Respondents with higher education were more likely to be exposed to and learn about emerging technologies. For the adoption of new technology in the construction industry, the actual operation and application of technology was more important for on-the-ground employees. These on-the-ground employees will be more aware of the actual applications and difficulties of a given technology.
4. Data Analyses and Results
4.1. Reliability and Validity
4.1.1. Reliability
When the Cronbach’s α coefficient of the variable was greater than 0.7, it indicated that the consistency of the measurement items corresponding to the variable was good. The reliability test results are presented in Table 3. The Cronbach’s α coefficient of each latent variable was greater than 0.7, indicating that the questionnaire designed in this study had a good reliability level.
4.1.2. Validity
Validity refers to the extent to which each measurement item explains the corresponding latent variables. Convergent validity refers to the degree to which measurement items corresponding to latent variables can fall into the same common variable. The convergence validity of the measurement model can be judged using average variance extraction (AVE) and composite reliability (CR). When AVE is greater than 0.5, CR is greater than 0.7, and the factor load of each item is greater than 0.5, the measurement model has good convergence validity. The measurement indices of the convergence validity of the measurement model in this study are listed in Table 4. The CR of all variables is greater than 0.7, and the AVE is also greater than 0.5; that is, the measurement model has good convergence validity.
Discriminant validity represents the degree of uncorrelation between potential variables. When the square root of the average variance extraction of variables is greater than the correlation coefficient between the variable and any other variable, the discriminant validity of the variables is ideal. Table 4 presents the discriminant validity table proposed in this study; the diagonal value is the square root of the mean variance extraction of the corresponding variable, and the value outside the diagonal is the correlation coefficient between the two variables. In general, the discriminant validity of each latent variable in this study can basically meet the requirements.
4.1.3. Analysis of Fit Degree
The analysis of the degree of fit mainly compares the variable relationships reflected by the model with those reflected by the collected valid data. In this study, the chi-squared degree of freedom ratio (CMIN/DF), goodness of fit index (GFI), comparative fitness index (CFI), root mean square residual (RMR), and root mean square error of approximation (RMSEA) were selected as criteria for judging the fitness of the model. The adaptation indicators in this study are shown in Table 5, of which only the RMR was slightly greater than the discriminant standard. The measurement model constructed in this study has a good degree of fit.
4.2. Hypothesis Testing Results and Effect
In this study, a structural model was established using AMOS software and is shown in Figure 3. The significance of the hypothesis was determined by the p and T values. When the p value was less than 0.05, or the T value is greater than 1.96, the hypothesis was considered statistically significant. The hypothetical test results are presented in Table 6. Among the 12 hypotheses proposed in this paper, H1a, H1b, H1c, H2, H3, H5, H6a, H7, and H8b pass the significance test. Among them, although the p value of H6b is significant and its regression coefficient is negative, contrary to the hypothesis in this study. To further explore the relationship between external and internal variables, this study conducted an effect analysis on the relationship between various variables; the results are shown in Table 7.
5. Discussions
Based on the TAM and TOE framework, this study extracted influencing factors from the three aspects of technology, organization, and environment and constructed a blockchain adoption model for the construction industry. The model was tested by collecting data through a questionnaire to analyze the adoption of blockchain in the construction industry. Further analysis of the research results from the internal, technical, organizational, and environmental dimensions of TAM is as follows:
5.1. The Impact of Perceived Usefulness and Perceived Ease of Use on Intention of Adopting Blockchain
Perceived usefulness and perceived ease of use were key variables in the TAM model. The empirical results of this study show that improving individuals’ perception of the usefulness and ease of use of blockchain can improve their intention of adopting blockchain. In addition, the effect of perceived usefulness on technology adoption intention (0.640) were greater than perceived ease of use (0.422), and perceived ease of use had a positive effect on perceived usefulness (0.363), which is consistent with the conclusion of previous empirical studies using TAM. This also proves that the model has a good explanatory ability [34,46]. As construction industry practitioners acquire a deeper understanding of the ease of use and usefulness of blockchains, they will highlight the technical features of blockchains to the needs of the development and construction as well as individual work efficiency, providing hope for their use in the organization.
5.2. The Impact of the Technological Dimension on the Adoption of Blockchain in the Construction Industry
The empirical results show that relative advantage, compatibility, and technological maturity play a significant role in influencing the adoption of blockchain in the construction industry. Relative advantage influences the intention of adopting blockchain through the intermediary of perceived usefulness. The blockchain of relative advantage is mainly reflected in the transparency, traceability, tamper-proof, and intelligent function. These technical features can play a role in data storage, supply chain management, bidding management, and other scenarios. The relative advantage of blockchain can effectively increase perceived usefulness and are the driving force for its active adoption in the construction industry. The empirical results of this study show that the compatibility and technological maturity of blockchain has a positive impact on perceived ease of use. The efficient flow of data information between various platform systems is one of the keys to the effective application of the blockchain system in the construction industry, which requires compatibility between the blockchain and the industry’s working mode, software, and system platforms. The more compatible blockchain is with the construction industry, the more the construction industry sees it as easy to use. The effect of technological maturity on perceived ease of use is 0.456, which is greater than other external variables. The maturity of blockchain technology and the mature industrial support in the construction field have greatly affected the construction industry’s perception of its ease of use. Mature blockchain products and services can improve the ease of use in the construction industry.
Adopting innovative technologies requires a certain cost. When the cost is too high, the use value is often reduced. Referring to Ullah and Kim’s research [24,47], this study hypothesizes that perceived cost of adoption negatively impacts the construction industry’s perceived usefulness of blockchain. However, empirical results do not support this. Considering that there is still a lack of blockchain products for the construction industry and the fact that blockchain is not popular in the construction industry, the lack of available precedent and ambiguity about the costs of blockchain adoption in the construction industry makes the impact of perceived cost of adoption on perceived usefulness insignificant in this paper.
5.3. Influence of Organizational Dimension on the Adoption of Blockchain in Construction Industry
The empirical results show that organizational readiness has a positive impact on perceived ease of use, which means that when the respondent’s organization has more resources to support the adoption of blockchain, they think that blockchain is easy to use. This result is consistent with the findings of Singh [34]. However, organizational readiness is contrary to the hypothesis of this study in that it affects perceived usefulness. Empirical results show that although this hypothesis passes the significance test (p = 0.046), it negatively affects perceived usefulness. This is contrary to the results of Chatterjee et al. [32] and Singh [34]. This means that when organizations provide more resources for adopting blockchain, the respondents’ perception of the usefulness of blockchain decreases. Unlike the adoption of other innovative technologies, blockchain is going to be a major revolution for the construction industry. It emphasizes decentralization, which will have a great impact on the working mode and inherent thinking mode of the traditional construction industry. In model innovation, management’s operational procedures, behavioral changes, and normative agreements will make it difficult for individuals to adapt, resulting in resistance [48]. When organizations prepare to adopt blockchain, it will undoubtedly change the way individuals currently work and inherently think. Individuals have become accustomed to the current way of working and will naturally resist the learning and application of blockchain technology, which makes them think that the blockchain is useless.
5.4. Impact of Environmental Dimension on the Adoption of Blockchain in Construction Industry
The empirical results show that competitive pressure has a positive impact on perceived usefulness, and among the external variables, competitive pressure has the greatest impact on perceived usefulness (0.573). Technological innovation is the main driving force for promoting industry development. When leading enterprises in the industry first apply blockchain and achieve good results, they will feel the pressure of competition and further feel the usefulness of the technology. Policies mainly refer to the guidance, incentive mechanisms, and mandatory policies adopted and issued by the government for the implementation of blockchain. Policy has a positive impact on perceived ease of use, indicating that the guidance and incentive policies issued by government departments for the implementation of blockchain can better enable construction practitioners to understand blockchain and how to implement it [49]. However, the impact of the policy on usefulness is not significant. It may be that the current incentive policy formulated by the government department is not enough to make the construction industry personnel feel that blockchain is useful to them, and the current policy is not strong enough.
6. Conclusions
Combined with the characteristics of the application of blockchain in the construction industry and TOE framework, this study extends the classical TAM and constructs an intention model of the construction industry to adopt blockchain. The main conclusions of this study are as follows: (1) the reliability and validity of the questionnaire passed the test, the coefficients of the model met the standard, and the overall fit of the model was good. The model constructed in this paper can effectively explain how various external factors affect construction practitioners’ perception of blockchain and ultimately their intention to adopt. Finally, relative advantage, compatibility, maturity of technology, organizational readiness, competitive pressure, policy, perceived ease of use, and perceived usefulness within TAM will have an impact on the adoption behavior of the construction industry. (2) The impact effect of perceived usefulness on adoption intention is 0.64, which is greater than that of perceived ease of use (0.422), and perceived ease of use also has a positive impact on perceived usefulness (0.363). (3) Although the organizational readiness can promote construction industry practitioners’ perception of the ease of use of the blockchain, it has a negative impact on adoption intention by negatively affecting the construction industry practitioners’ perception of the usefulness of the blockchain (the total effect of the impact is −0.03). Among the other external variables, competitive pressure has played the largest role in promoting construction industry practitioners’ perception of usefulness (0.573). The maturity of blockchain plays the largest role in improving the construction industry practitioners’ perception of blockchain ease of use (0.456).
6.1. Research Contributions
6.1.1. Theoretical Contributions
This study combines the technology adoption theory with the TOE framework and applies it to the research on the adoption behavior of blockchain in the construction industry and builds an adoption model. By selecting external variables suitable for the characteristics of the construction industry and blockchain, the application of technology adoption theory is expanded, and it also makes theoretical contributions to the research on blockchain adoption behavior in the construction industry.
6.1.2. Practical Implications
The influence of three dimensions of technology, organization, and environment on the adoption of blockchain in the construction industry have been analyzed in detail, and the study conclusions may bring enlightenment to the promotion and application of blockchain in the construction industry, for example, by improving the maturity of blockchain products and supporting facilities in the construction industry, improving the ease of use of blockchain, or by establishing exemplary enterprises, highlighting the competitive advantages brought by blockchain applications and thereby improving the construction industry’s perceived usefulness of blockchain.
6.2. Limitations and Future Research
Although some achievements have been made, the study still has the following shortcomings: (1) TAM implicitly assumes that the adoption and use of new technologies by individuals is voluntary, but in fact, individuals are affected by internal interventions in the organization and social factors, and the adoption of new technologies is often passive. (2) The small proportion of respondents who are familiar with blockchain technology and have extensive management experience affects the empirical investigation of this study. However, since blockchain is an emerging technology and not widely used in the construction industry, it is difficult to obtain enough respondents who are familiar with blockchain technology. Subsequent research needs to further optimize the research sample and more fully integrate the practical applications of blockchain in the construction industry. (3) Construction projects in the construction industry usually involve multiple stakeholders, which have not been classified in this study. Future research work can further explore how different types of construction industry organizations differ in their blockchain adoption behavior.
Conceptualization, X.W.; Data curation, L.L. and X.H.; Formal analysis, X.H.; Investigation, L.L.; Methodology, X.H.; Supervision, J.L.; Validation, J.L.; Writing—original draft, L.L.; Writing—review & editing, X.W. All authors have read and agreed to the published version of the manuscript.
The datasets used and/or analyzed during the current study are available from the corresponding author on request (Jingkuang Liu).
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Measurement instrument.
| Constructs | Measurement Item | Sources |
|---|---|---|
| Perceived usefulness | PU1: Using blockchain technology can improve my productivity | [ |
| PU2: Using blockchain technology can improve the quality of my work | ||
| PU3: The application of blockchain technology is very helpful for my work | ||
| Perceived ease of use | PEU1: Programs and system software related to blockchain technology are easy for me to learn and operate | [ |
| PEU2: It is easy to apply the functions of blockchain to my work | ||
| PEU3: The interaction between blockchain technology and other technologies is clear | ||
| Intention to use | IU1: I support the adoption of blockchain technology in my organization | [ |
| IU2: I am willing to actively participate in the promotion and application of blockchain technology in the construction industry | ||
| IU3: I hope to use blockchain technology more in my future work | ||
| Perceived cost of adoption | PC1: Adoption of blockchain technology requires high hardware and software facility costs | [ |
| PC2: Adoption of blockchain technology requires high personnel training and recruitment costs | ||
| PC3: Investing in blockchain technology is stressful for my organization | ||
| Relative advantage | RA1: The openness and transparency of blockchain can promote data sharing and improve the utilization of data information | [ |
| RA2: The smart contract function of blockchain can improve work efficiency and ensure the performance of contract obligations | ||
| RA3: The non-tampering and non-traceable characteristics of blockchain are conducive to ensuring the authenticity and reliability of data information | ||
| RA4: The consensus mechanism and decentralization of blockchain can improve the cooperation and trust of project participants | ||
| Compatibility | CA1: Blockchain technology is well compatible with the existing equipment and software infrastructure in the construction industry | [ |
| CA2: Blockchain technical features are well compatible with the current working mode of the construction industry | ||
| CA3: The use of blockchain conforms to the culture and values of the construction industry | ||
| Technological maturity | TM1: Blockchain technology itself has been developed and improved, and the blockchain system is very stable | [ |
| TM2: There are standards and guidelines for the application and work of blockchain technology | ||
| TM3: Blockchain technology suppliers can provide perfect products, supporting facilities and service values | ||
| Organizational readiness | ORE1: The enterprise has a plan and organization to train and reserve professionals related to blockchain technology | [ |
| ORE2: The enterprise has the special funds to adopt blockchain technology | ||
| ORE3: The enterprise has the facilities and equipment to adopt blockchain technology | ||
| ORE4: The senior management of the company is willing to provide necessary support and help when the adoption of blockchain encounters difficulties | ||
| Competitive pressure | CP1: Cooperative enterprise or contractual provisions require the use of blockchain technology in cooperative projects | [ |
| CP2: With the adoption of blockchain technology, the peer will be more competitive in the market | ||
| CP3: With fierce competition in the industry, the enterprise must adopt blockchain technology to ensure that it will not be eliminated by competition | ||
| Policy | PO1: The government encourages the construction industry to adopt blockchain technology and apply it in the supervision and carbon emission management of the construction industry, and provides tax exemptions and subsidies to construction enterprises that adopt blockchain technology | [ |
| PO2: To improve the supervision and carbon emission management of the construction industry, Government departments require construction enterprises to adopt blockchain technology | ||
| PO3: Government departments have formulated relevant guidelines and application standards for the implementation of blockchain technology in the construction industry |
The basic information.
| Basic Information | Category | Frequency | Percentage |
|---|---|---|---|
| Gender | Male | 177 | 69.1 |
| Female | 79 | 30.9 | |
| Years of work in the construction industry | 3 years or less | 121 | 47.3 |
| 3–5 years | 36 | 14.1 | |
| 6–10 years | 31 | 12.1 | |
| 11–15 years | 48 | 18.8 | |
| More than 20 years | 20 | 7.8 | |
| Work units | Universities and scientific research institutions | 8 | 3.1 |
| Construction unit | 54 | 21.1 | |
| Survey and design unit | 34 | 13.3 | |
| Other units | 78 | 30.5 | |
| Construction unit | 58 | 22.7 | |
| Consulting unit | 24 | 9.4 | |
| Highest education | College and below | 33 | 12.9 |
| Undergraduate | 166 | 64.8 | |
| Master | 52 | 20.3 | |
| PhD | 5 | 2 | |
| Layers of management | Non-management positions | 85 | 33.2 |
| On-the-ground managers | 82 | 32 | |
| Middle managers | 62 | 24.2 | |
| Top management | 27 | 10.5 | |
| Your organization’s use of blockchain | No adoption plans yet | 153 | 59.8 |
| On the sidelines | 76 | 29.7 | |
| Planning started | 17 | 6.6 | |
| Applying | 10 | 3.9 |
Reliability and convergence validity indices.
| Variables | Items | Loadings | Cronbach’s α | CR | AVE |
|---|---|---|---|---|---|
| Relative advantage | RA4 | 0.76 | 0.879 | 0.883 | 0.649 |
| RA3 | 0.865 | ||||
| RA2 | 0.824 | ||||
| RA1 | 0.78 | ||||
| Compatibility | CA3 | 0.827 | 0.884 | 0.886 | 0.721 |
| CA2 | 0.869 | ||||
| CA1 | 0.851 | ||||
| Perceived cost of adoption | PC3 | 0.587 | 0.773 | 0.787 | 0.558 |
| PC2 | 0.839 | ||||
| PC1 | 0.79 | ||||
| Technological maturity | TM3 | 0.755 | 0.814 | 0.806 | 0.573 |
| TM2 | 0.823 | ||||
| TM1 | 0.707 | ||||
| Organizational Readiness | ORE3 | 0.889 | 0.905 | 0.901 | 0.715 |
| ORE2 | 0.927 | ||||
| ORE1 | 0.789 | ||||
| ORE4 | 0.719 | ||||
| Competitive pressure | CP3 | 0.811 | 0.825 | 0.835 | 0.603 |
| CP2 | 0.824 | ||||
| CP1 | 0.739 | ||||
| Policy | PO3 | 0.861 | 0.893 | 0.895 | 0.740 |
| PO2 | 0.897 | ||||
| PO1 | 0.821 | ||||
| Perceived ease of use | PEU1 | 0.743 | 0.847 | 0.847 | 0.666 |
| PEU2 | 0.874 | ||||
| PEU3 | 0.794 | ||||
| Perceived usefulness | PU3 | 0.873 | 0.904 | 0.907 | 0.755 |
| PU2 | 0.878 | ||||
| PU1 | 0.873 | ||||
| Intention to use | IU1 | 0.897 | 0.909 | 0.91 | 0.771 |
| IU2 | 0.881 | ||||
| IU3 | 0.855 |
Discriminant validity of variables.
| Variables | PO | CP | ORE | TM | PC | CA | RA | PEU | PU | IU |
|---|---|---|---|---|---|---|---|---|---|---|
| PO | 0.860 | |||||||||
| CP | 0.763 | 0.776 | ||||||||
| ORE | 0.516 | 0.742 | 0.846 | |||||||
| TM | 0.628 | 0.733 | 0.735 | 0.757 | ||||||
| PC | 0.326 | 0.153 | 0.123 | 0.229 | 0.747 | |||||
| CA | 0.637 | 0.675 | 0.560 | 0.624 | 0.378 | 0.849 | ||||
| RA | 0.475 | 0.353 | 0.130 | 0.312 | 0.419 | 0.585 | 0.805 | |||
| PEU | 0.654 | 0.714 | 0.706 | 0.725 | 0.245 | 0.666 | 0.344 | 0.816 | ||
| PU | 0.752 | 0.762 | 0.573 | 0.693 | 0.315 | 0.693 | 0.521 | 0.766 | 0.869 | |
| IU | 0.605 | 0.662 | 0.501 | 0.596 | 0.248 | 0.570 | 0.398 | 0.680 | 0.785 | 0.876 |
Model fit test.
| Indicators | Criteria | Measurement Mode |
|---|---|---|
| GFI | >0.8 | 0.837 |
| RMR | <0.05 | 0.052 |
| RMSEA | <0.08 | 0.059 |
| NFI | >0.8 | 0.874 |
| IFI | >0.9 | 0.937 |
| TLI | >0.9 | 0.926 |
| CFI | >0.9 | 0.936 |
| PGFI | >0.5 | 0.683 |
| CMIN/DF | <3 | 1.894 |
Hypothesis testing.
| Hypothesis | Estimate | T-Value | p-Value | Result | |
|---|---|---|---|---|---|
| H1a | Perceived usefulness has a positive effect on intention to use | 0.640 | 6.937 | 0.000 | Supported |
| H1b | Perceived ease of use has a positive effect on intention to use | 0.190 | 2.088 | 0.037 | Supported |
| H1c | Perceived ease of use has a positive effect on perceived usefulness | 0.363 | 4.398 | 0.000 | Supported |
| H2 | Relative advantage has a positive effect on perceived usefulness | 0.152 | 2.698 | 0.007 | Supported |
| H3 | Compatibility has a positive effect on perceived ease of use | 0.173 | 2.373 | 0.018 | Supported |
| H4 | Perceived cost of adoption has a negative effect on perceived usefulness | 0.073 | 1.393 | 0.164 | Not Supported |
| H5 | Technological maturity has a positive effect on perceived ease of use | 0.456 | 4.359 | 0.000 | Supported |
| H6a | Organizational readiness has a positive effect on perceived ease of use | 0.192 | 2.419 | 0.016 | Supported |
| H6b | Organizational readiness has a positive effect on perceived usefulness | −0.173 | −1.997 | 0.046 | Not Supported |
| H7 | Competitive pressure has a positive effect on perceived usefulness | 0.573 | 4.534 | 0.000 | Supported |
| H8a | Policy positively has a positive effect on usefulness | 0.071 | 0.796 | 0.426 | Not Supported |
| H8b | Policy has a positive effect on ease of use | 0.158 | 2.153 | 0.031 | Supported |
Summary of impact effects.
| Variables | Total Effect | Direct Effect | Indirect Effect | ||||||
|---|---|---|---|---|---|---|---|---|---|
| PEU | PU | IU | PEU | PU | IU | PEU | PU | IU | |
| PC | 0.158 | 0.129 | 0.112 | 0.158 | 0.071 | - | - | 0.057 | 0.112 |
| CP | - | 0.573 | 0.367 | - | 0.573 | - | - | - | 0.367 |
| ORE | 0.192 | −0.104 | −0.030 | 0.192 | −0.173 | - | - | 0.070 | −0.030 |
| TM | 0.456 | 0.165 | 0.192 | 0.456 | - | - | - | 0.165 | 0.192 |
| PO | - | 0.073 | 0.047 | 0.000 | 0.073 | - | - | - | 0.047 |
| CA | 0.173 | 0.063 | 0.073 | 0.173 | - | - | - | 0.063 | 0.073 |
| RA | - | 0.152 | 0.097 | - | 0.152 | - | - | - | 0.097 |
| PEU | - | 0.363 | 0.422 | - | 0.363 | 0.190 | - | - | 0.232 |
| PU | - | - | 0.640 | - | - | 0.640 | - | - | - |
| IU | - | - | - | - | - | - | - | - | - |
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