1. Introduction

2. Research review

3. Construction of the digital transformation maturity evaluation model (DTCMM) for construction enterprises

3.2. Description of specific indicators

3.2.1. Digital strategy

Digital strategies can be grouped into distinct categories: the alignment level A1 of digital strategic planning, the commitment level A2 to implementing digital transformation, and the extent of government policy support A3. The alignment level A1 assesses whether an enterprise’s digital transformation strategy corresponds to its long-term goals and technical capabilities, emphasizing the need for a tailored strategy to enhance efficiency and clarity [31]. The commitment level of A2 gauges an enterprise’s determination and vigor in executing its digital transformation strategy [32]. As digital transformation is gradual, an enterprise’s support significantly impacts its pace and effectiveness [33]. Greater investment signals stronger commitment, fostering more effective leadership and supervision of the transformation progress [34]. The government’s policy support for A3 reflects the resource allocation for digital transformation by the enterprise’s local government department, indicating government encouragement [35].

3.2.2. Digital business applications

Digital business applications are classified into four groups: human resource planning B1, business contract management B2, production technology management B3, and quality and safety management B4. Human resource planning B1 assesses the existence of a comprehensive human resource information management system, enhancing decision-making, personnel deployment, cost efficiency, and overall human resource structure analysis [36]. Business contract management B2 evaluates material and business proficiency, incorporating contract, cost, material, subcontractor, settlement management, driving productivity, cost reduction, competitive advantage, and overall benefits [37]. Production technology management B3 evaluates the integration of information technology like IoT, big data, digital twins, and BIM with construction site management, enhancing on-site capabilities and competitiveness [38]. Quality and safety management B4 assesses real-time tracking of project quality and safety indicators, spanning monthly quality reports, inspections, analysis, statistics, and safety measures, fostering high-quality development through quality, safety, and facility management [39].

3.2.3. Digital technology capabilities

The digital technology capabilities category is segmented into five sections: new technical personnel C1, digital infrastructure C2, improvement in digital project integration management platform degree C3, degree of integration of digital technology and construction site C4, and digital innovation iteration ability C5. New technical personnel C1 signifies the enterprise’s human resource reservoir, where employees experienced in digital system development or operational processes play a pivotal role in enhancing digital technology capabilities. Digital infrastructure C2 denotes the enhancement of foundational IT infrastructure, including 5G networks, as complete infrastructure expedites digital technology implementation due to high hardware and software prerequisites [40]. The degree of improvement in the digital project integration management platform C3 reflects an enterprise’s familiarity with the project’s entire lifecycle, while an encompassing management platform amplifies productivity [41]. The degree of integration of digital technology and construction site C4 highlights whether digital transformation results suit construction site needs, align with smart construction site functions, and effectively combine digital technology with construction tools. Digital innovation iteration ability C5 underlines R&D innovation competence and adeptness in timely software updates [42].

3.2.4. Data capabilities

The data capability is divided into three sections: data collection and processing D1, data analytics capability D2, and data security D3. Data collection and processing D1 assesses if the enterprise gathers comprehensive and real-time data, while processing obtains the most representative data [43]. Data analytics capability D2 gauges whether the enterprise comprehensively and logically interprets collected data ensures accurate data comprehension, and effectively mines information from the data [44]. Data security D3 signifies the enterprise’s measures to safeguard data securely and legally compliantly, including continuous security maintenance, covering both data source and data protection security [45].

3.2.5. Digital organizational capabilities

Digital organizational capabilities encompass three factors: organizational mechanism and process of enterprise digitalization E1, corporate culture E2, and employees’ commitment to digital transformation E3 [40]. The organizational mechanism and process E1 gauge the alignment of the enterprise’s structure with digital transformation and the feasibility of long-term digital projects. Corporate culture E2 mirrors decision-makers’ stance on innovation, which can range from conservative to innovative. Diverse cultures influence transformation decisions [42]. Employees’ commitment to digital transformation E3 impacts transformation efficiency and progress; heightened employee engagement enhances overall transformation quality and efficiency [46,47].

3.2.6. Change management

Change management encompasses digital transformation management mode F1 and change manager skills F2. The digital transformation management mode F1 evaluates the effectiveness of the enterprise’s oversight and control over digital transformation to address potential issues [48]. Change manager skills F2 assesses whether employees are well-versed in the entire transformation process and capable of identifying and promptly addressing potential or ongoing issues in change management [49].

Table 2. List of factors influencing the maturity of digital transformation in the construction industry.

Table 2. List of factors influencing the maturity of digital transformation in the construction industry.

Evaluation model of digital transformation maturity of construction enterprises (A)	First-Level	Number	Second-Level	Number
	Digital strategy	B1	Digital strategic planning matching degree The intensity of enterprises to promote digital transformation Government department policy incentives and support strength	C11 C12 C13
	Digital industry Application	B2	HRM Business contract management Productive technology management Quality and safety management	C21 C22 C23 C24
	Digital technology capabilities	B3	New technical personnel Digital infrastructure Digital management platform function degree Digital technology and the construction site integration degree Digital innovation and iteration ability	C31 C32 C33 C34 C35
	Data ability	B4	Data collection and processing capacity Data analysis ability Data security	C41 C42 C43
	Digital organizational ability	B5	Digital organizational structure and processes Corporate culture The degree of employee digital transformation	C51 C52 C53
	Change management	B6	Digital management mode Change management staff skills	C61 C62

4. Analysis of influencing factors of digital transformation maturity of construction enterprises based on AHP-DEMATEL

4.1. Specific steps of the AHP method

4.1.1. Build hierarchical models

This study focuses on large-scale construction enterprises primarily involved in municipal public works and housing construction projects, encompassing areas like roads, bridges, and tunnels. These enterprises are extensively engaged in architectural, structural, and mechanical-electrical design, capable of undertaking diverse construction projects and offering top-notch services and solutions. By organizing indicator relationships and considering enterprise realities and research scope, a digital transformation maturity evaluation model for construction enterprises is established by classifying indicators into target and criterion layers.

4.1.2. Determination of judgment matrix

This study assembles a panel of experienced experts to conduct pairwise comparisons between influencing factors from the criterion and factor layers, assigning importance scores on a scale of 1 to 5 (refer to Table 3). Subsequently, an influence factor judgment matrix A =, i, j = 1, 2,..., n is formulated, where A represents the importance of element i within the same criterion or factor layer relative to element j (as displayed in Table 3). Table 4 serves as the evaluation system judgment matrix A.

4.1.3. Compute eigenvalues and eigenvectors

Use the sum product method to obtain the eigenvalues λ max and eigenvector W of the judgment matrix A. The vector value corresponding to the eigenvector is the weight of each element relative to the upper element. The calculation steps are:

(1) Normalize each column in the judgment matrix to obtain $\overline{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}$

$$\overline{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}=\frac{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}{\sum _{k=1}^n{a}_k},(i,j=\mathrm{1,2},···，n)$$

(2) Add the normalized elements in rows to obtain a vector $\overline{W}={(\overline{W_1},\overline{W_2},···\overline{W_n})}^T$

$$\overline{W_i}=\sum _{j=1}^n\overline{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}},(i,j=\mathrm{1,2},3,···)$$

(3) The normalization process is performed to obtain the eigenvector$W={(W_1{,W}_1,···W_n)}^T$

$$W_i=\frac{\overline{W_i}}{\sum _{i=1}^n\overline{W_i}}$$

Calculate the eigenvector $W_i$=${(0.597，1.772，1.29，1.147，0.597，0.597)}^T$

(4) Calculate the characteristic roots

$$\begin{gathered} \mathrm{U}=\mathrm{AW} \\ \mathrm{U}=\left(\begin{array}{cccccc}1& 1/3& 1/2& 1/2& 1& 1/2\\ 3& 1& 1& 2& 3& 3\\ 2& 1& 1& 1& 2& 2\\ 2& 1/2& 1& 1& 2& 2\\ 1& 1/3& 1/2& 1/2& 1& 1\\ 2& 1/3& 1/2& 1/2& 1& 1\end{array}\right)*\left(\begin{array}{c}0.597\\ 1.772\\ 1.29\\ 1.147\\ 0.597\\ 0.597\end{array}\right)=\left(\begin{array}{c}3.600\\ 10.729\\ 7.791\\ 6.905\\ 3.600\\ 3.600\end{array}\right) \end{gathered}$$

(5) Calculate the maximum eigenvalue of the judgment matrix Aλmax

$$\mathsf{\lambda }\max =\frac{1}{\mathrm{n}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\frac{{\left(AW\right)}_i}{W_i}$$

$$\mathsf{\lambda }\max =\frac{1}{\mathrm{n}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\frac{{\left(AW\right)}_i}{W_i}=\frac{1}{6}(\frac{3.6}{0.597}+\frac{10.729}{1.772}+\frac{7.791}{1.29}+\frac{6.905}{1.147}+\frac{3.6}{0.597}+\frac{3.6}{0.597}$$

)=6.034

4.1.4. Level single-order consistency check

The consistency index CI, consistency ratio CR, and average random one-time index RI are introduced. When CR < 0.1, the consistency test of the index is satisfied. The RI values are shown in the Table 5, and n is the order of the judgment matrix.

$$\mathrm{C}\mathrm{I}=\frac{(\mathsf{\lambda }\mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{n})}{(\mathrm{n}-1)}=\frac{6.034-6}{6-1}=0.0068$$

Table 4 shows that when n = 6, the RI value is 1.26.

$$\mathrm{C}\mathrm{R}=\frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}=\frac{0.0068}{1.26}=0.0054<0.1$$

Therefore, the random one-time ratio CR = 0.0054 < 0.1 meets the consistency requirement.

4.1.5. Calculation weights

Compute the weight of the criterion layer concerning the target layer and the weight of the factor layer concerning the criterion layer. The index weight of the criterion layer forms a feature vector, and the factor layer’s weight relative to the criterion layer is directly given here. Subsequently, multiply the weights associated with each target layer factor by the criterion layer’s weight to derive the basic weights of each factor in the factor layer, denoted as W¹.

Table 6. Basic weights of primary indicators.

Table 6. Basic weights of primary indicators.

One-Level Metric	Digitalize Strategy (B1)	Digitalize Business Application (B2)	Digitalize Technical Capability (B3)	Data Ability (B4)	Figure Organizing Ability (B5)	Transform Management (B6)	Weight (%)
Digitalize Strategy (B1)	1	1/3	1/2	1/2	1	1	9.95
Digitalize Business Application (B2)	3	1	1	2	3	3	29.53
Digitalize Technical Capability (B3)	2	1	1	1	2	2	21.50
Data Ability (B4)	2	1/2	1	1	2	2	19.11
Figure Organizing Ability (B5)	1	1/3	1/2	1/2	1	1	9.95
Transform Management (B6)	1	1/3	1/2	1/2	1	1	9.95

λ max=6.034 CI=0.0068 RI=1.26 CR=0.0054 <0.1 passed the random consistency test.

4.1.6. Calculate the weight of indicators at all levels

Using the AHP to calculate the weight of the comprehensive evaluation index system.

Table 7. Weights of the comprehensive evaluation index system${\mathrm{W}}^1.$

Table 7. Weights of the comprehensive evaluation index system${\mathrm{W}}^1.$

Standard Layer (Level I Index)	Weight (%)	Index Layer (Secondary Index)	Weight (%)	Base Weight ${\mathbf{W}}^1$(%)
B1	9.95	C11	49.05	4.88
		C12	19.76	1.97
		C13	31.19	3.10
B2	29.53	C21	16.46	4.86
		C22	20.63	6.09
		C23	34.17	10.09
		C24	28.75	8.49
B3	21.50	C31	13.51	2.90
		C32	7.85	1.69
		C33	12.71	2.73
		C34	39.60	8.51
		C35	26.33	5.66
B4	19.11	C41	31.19	5.96
		C42	49.05	9.37
		C43	19.76	3.78
B5	9.95	C51	31.19	3.10
		C52	19.76	1.97
		C53	49.05	4.88
B6	9.95	C61	66.67	6.63
		C62	33.33	3.32

4.4. Comprehensive assessment calculation method

To ensure accurate measurement of each domain’s implementation level, the questionnaire comprises 1 to 5 questions per domain, with respondents assigning scores of 0 to 4 to both digital level and importance. Averaging valid questionnaire domain scores yields comprehensive domain scores. For instance, taking domain A1 as a case, the final score of the digital transformation maturity is given by

where ${\mathrm{S}}_{\mathrm{A}1}$ is the evaluation value of domain A1, k is the number of questions, ${\mathrm{s}}_{\mathrm{i}}$ is the digital level score of question i, ${\mathrm{w}}_{\mathrm{i}}$is its importance, and n is the number of indicators in the domain.

Consider a construction company specializing in roads, bridges, tunnels, and housing. The company was among the first to use digital technology due to national policies. Adopting digital methods encountered various challenges, making it a good representation of the construction industry in China. The company’s efforts to enhance its digital capabilities focused on areas like managing construction (including quality, safety, and progress), using smart construction sites, and integrating Building Information Modeling (BIM). The company collaborated with parties like supervisors, builders, and testers. They built a comprehensive digital platform that connected all parts of the construction process from start to finish. This helped digitize and make construction management smarter in controlling costs, tracking progress, ensuring quality, maintaining safety, and monitoring the environment. By combining technologies like BIM, Geographic Information Systems (GIS), the Internet of Things (IoT), and mobile Internet, the company managed on-site personnel, machinery, materials, production processes, and more in real time. They used data collection and analysis to monitor, analyze, and mine information automatically. This enabled real-time monitoring, early warnings, safety checks, performance evaluations, and quick emergency response. The company evaluated and scored various factors across its operations, following the guidelines in Table 13. These scores helped determine the overall performance in different areas.

Table 13. Example of case enterprise digital maturity evaluation questionnaire.

Table 13. Example of case enterprise digital maturity evaluation questionnaire.

Question	Score	The Meaning of the Score
Do you think the digital strategy designated by your company is suitable for the current development situation of the enterprise?	0	Fully not in line with the company’s current development of the situation
	1	This is not in line with the current development situation of the company
	2	Generally in line with the company’s current development
	3	In line with the company’s current development of the situation
	4	This is very much in line with the current development of the company
Do you think your company has designated digitalWhether the strategy can be realized?	0	It cannot be achieved at all
	1	It cannot be fully realized
	2	To be able to implement a part of it
	3	To achieve the most
	4	Can be realized

Table 14. Weights and scores of each index of the digital maturity evaluation model of case enterprises.

Table 14. Weights and scores of each index of the digital maturity evaluation model of case enterprises.

One-Level Metric	Single Layer Weight (%)	Two-Stage Metric	Single Layer Weight (%)	Foundation Weight (%)	Assemble Weight (%)	Synthesize Grade	Assemble Grade
Digitalize strategy (B1)	9.95	C11	49.05	4.88	4.90	2.2	0.108
		C12	19.76	1.97	2.13	1.8	0.038
		C13	31.19	3.10	2.45	2.6	0.064
Digitalize business application (B2)	29.53	C21	16.46	4.86	4.88	2	0.098
		C22	20.63	6.09	6.46	2.2	0.142
		C23	34.17	10.09	11.10	1.2	0.133
		C24	28.75	8.49	9.17	1.2	0.110
Digitalize technical competence (B3)	19.11	C31	13.51	2.90	3.41	0.8	0.027
		C32	7.85	1.69	1.86	2.0	0.037
		C33	12.71	2.73	3.05	1.0	0.031
		C34	39.60	8.51	9.19	1.0	0.092
		C35	26.33	5.66	6.01	0.8	0.048
Data ability (B4)	18.99	C41	31.19	5.96	6.32	0.6	0.038
		C42	49.05	9.37	10.67	0.6	0.064
		C43	19.76	3.78	3.21	0.8	0.026
Digital organization ability (B5)	9.95	C51	31.19	3.10	1.97	1.6	0.032
		C52	19.76	1.97	1.10	1.4	0.015
		C53	49.05	4.88	5.08	1.2	0.061
Change management (B6)	9.95	C61	66.67	6.63	5.12	1.0	0.051
		C62	33.33	3.32	1.92	0.8	0.015

By calculating the overall digital maturity score for the example enterprise as 1.23, it is evident that the enterprise is currently in the stage of process operation level in its digital transformation journey. This implies that the company uses digital technology to integrate property, finance, and taxation aspects, aiming to enhance operational quality and capabilities continuously. The company utilizes intelligent data analysis and risk early warning to support strategic planning, risk management, target setting, performance evaluation, and decision-making. However, there is room for improvement in digital capabilities, limiting the company’s growth potential. To progress further, a deeper and more comprehensive digital transformation is necessary. Within the construction management process, digital technology is employed to reduce construction timelines, lower costs, enhance project quality, and expedite the move toward intelligent construction practices.

5. Evaluation results and analysis

6. Conclusion

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