Mapping the Digital Transformation Maturity of the Building Construction Industry Using Structural Equation Modeling

Abstract

Construction projects play a vital role in the global economy. However, the construction industry still lags in the adoption of digital technologies which have the potential to alleviate inefficiencies. There is also a lack of construction professionals with skills to implement digital technologies, and the industry is plagued by inadequate research and development (R&D) and low productivity. This paper applies the Digital Transformation Readiness Level Index in Building Construction (DTRLIIBC) to investigate digital transformation in the construction industry, and specifically in the construction phase, by identifying technologies, alternatives, policy incentives to ensure best practices, and infrastructure needed to smoothly implement digital technologies. A survey was distributed to executive managers, department managers, project managers, senior engineers, and supervisors in the construction industry. Interviews were also conducted with 13 experts with more than 20 years of experience, using the Delphi method to ensure the validity and reliability of the factors identified as significant based on their experience and perspectives. This study thus assessed potential factors related to digital transformation, along with identifying factors enabling the use of the DTRLIIBC itself. Structural Equation Modeling (SEM) was employed to identify causal relationships among the variables while minimizing measurement errors, as SEM was considered a multiple regression analysis to improve the efficiency of the model. To ascertain the model’s robustness and stability, a comprehensive evaluation was conducted that included tests for multivariate normality conformance, validity and reliability assessments, and accuracy evaluations to improve overall performance in the construction industry. The benefit of implementing SEM is its ability to provide a clear decision-making alternative as well as a potential vision for construction industry firms to improve efficiency and productivity in construction projects.

1. Introduction

The global adoption of digital technologies has been very beneficial to the construction sector. A wide range of digital technologies have been recently applied to the building life cycle, including include robotics, drones, mobile devices, smart data, cloud computing, augmented reality, BIM, 3D printing, artificial intelligence (AI), big data, and the Internet of Things (IoT) [1]. However, these technologies often remain siloed due to the fragmentation of the construction sector, rather than being adopted across the wider industry. This highlights the need for integration.

Thus, to reduce the impact of fragmentation and guarantee performance and productivity in the construction industry, a digital transformation roadmap is required. The use of digital technologies has greatly benefited the construction sector, increasing efficiency and productivity in several related areas. Digital transformation has had an influence on every stage of construction projects’ lifespan, from planning and development to facility management. Digital transformation strengthens the integration of people, processes, and context within the built environment, improves the detection of possible design and construction concerns, and makes stakeholder engagement easier in the execution phase [2,3,4].

As [5] elucidated, the Cyber–Physical System (CPS) framework, which is based on five key development environments, has shown noteworthy benefits in terms of project completion timelines and quality. Nevertheless, ref. [6] noted that the fragmented structure of the building industry, the complexity of projects, and the lack of pertinent norms and regulations present obstacles to the implementation of such new technology in this sector. Meanwhile, ref. [7] asserted that efforts towards digital transformation should be given top priority by higher management in the construction industry and included in vision and mission statements in both the public and private sectors. However, as [8] showed, 63% of Nigerian construction companies are content with their organization’s readiness, digital transformation, and digital technology capabilities, despite not having advanced beyond the foundation level. This paper investigates the use of digital transformation in the building construction phase in terms of the vertical aspect regarding of building projects.

The research questions are as follows:

• What are the factors that influence the adoption of digital transformation in the construction phase of building projects?
• What tools and assessments can be used to evaluate the digital transformation readiness level index to assess building construction?

Despite the potential to enhance the overall performance, the construction industry has been hesitant to incorporate digital transformation. The objective of this research is to evaluate the digital transformation readiness level index in building construction, alongside SEM to analyze the critical success factors obtained from the literature review. It examines prior research on the application of digital transformation in the construction industry, with particular emphasis on the construction phase. It also highlights the benefits, different approaches, and obstacles that hinder the adoption of digital transformation in the industry that have been the subject of previous research. Nevertheless, there is a need for comprehensive global analysis of the utilization, advantages, and challenges to the adoption of digital technologies during the construction phase. This paper categorizes the digital transformation readiness level in building construction according to the potential technology, policy, and infrastructure-related factors obtained from the extensive literature review analysis. This study is limited to the vertical aspect of digital transformation in building construction.

This paper’s structure is as follows: Section 1 serves as the introduction, highlighting the crucial role and concept of digital transformation in building construction. Section 2 discusses the extensive literature review on digital transformation in building construction during the execution phase. Section 3 depicts the digital transformation readiness SEM for the building construction execution phase. Section 4 presents the research identification, method, data collection, and interpretation details. Section 5 presents the structural equation model. Section 6 presents the survey data analysis. Section 7 presents a discussion of the SEM results. Finally, Section 8 presents the conclusion.

2. Literature Review

2.1. The Concept of Digital Transformation in the Construction Industry in the Execution Phase

This section illustrates some of the technological principles that have been applied in the construction industry. An extensive body of the literature has documented a wide range of digital transformation approaches that have introduced new technologies in the construction industry. These technologies include drones, mobile and wearable devices, robotics, virtual reality (VR), augmented reality (AR), mixed reality (MR), 3D printing, cloud computing, AI, big data analytics, and the IoT. The incorporation of these technologies is crucial in transforming the construction sector, providing a strong basis for supporting extensive digital transformation projects throughout the construction industry. Importantly, the existing literature concerning technological readiness in the construction sector is inconsistent, with the majority of research focusing on the implementation of specific digital technologies. Some investigations, such as those by were limited to the identification of barriers impeding the technological adoption of individual technologies [9,10]. In recent times, the construction sector has encountered increasingly complex challenges, notably those stemming from large-scale projects, which have prompted substantial changes through the adoption of various technological solutions [11]. Robotic laser scanning [12], AI-BIM [13], and AR–BIM–robotics [14] are significant examples of the integration of multiple technologies in the construction industry. However, many construction firms still lack the direction and resources that would help them accurately evaluate their performance in adopting new technologies and empower them to achieve technological readiness or competence [15]. As an illustration, the research conducted acknowledges the significant role digital transformation has had in the Sri Lankan construction sector’s considerable gains in productivity. However, the development of a tool to assess the Sri Lankan construction industry’s readiness for digital transformation remains unfinished [16].

Digital transformation has the potential to significantly transform the construction industry, but its implementation and integration have been comparatively slower than in other industries, such as manufacturing. Numerous factors, including the increasing complexity of construction sites and projects, the discrete nature of the industry, and an overall reluctance to adopt novel technologies, have contributed to the lack of rapid advancement [17]. However, an increasing number of researchers agree that construction companies can gain substantial market advantages by proactively anticipating and implementing new technologies [18,19]. Recent research by [5,20] has emphasized the significance of recognizing and confronting obstacles and identifying efficacious approaches in order to enhance the implementation and practicality of digital technologies within the construction industry. This will improve data transfer and stakeholder collaboration and reduce industry fragmentation.

2.2. The Challenges of Adopting DT in the Construction Industry

The construction industry’s reluctance to adopt technological advancements is described in the [21]. A comprehensive list of obstacles preventing the adoption of the construction 4.0 framework can fundamentally change from Design, construction to operation phase as well as construction 4.0 has been compiled by researchers based on an exhaustive review of the relevant literature [1,22,23,24]. These obstacles include opposition to change, uncertainty regarding the value proposition, high costs, insufficient investment in R&D, skill gaps in the profession, longitudinal fragmentation, the absence of standardized protocols, data security, protection, cybersecurity concerns, and legal and contractual ambiguities.

As a result of these challenges, the implementation of digital technologies in the construction industry remains limited, particularly at the local level [25]. In addition to limited funding and a lack of awareness, the absence of regulatory oversight regarding the implementation of innovative technologies by regional construction companies further impedes the industry’s adoption of new technologies. As a result, the majority of construction firms continue to rely on conventional approaches.

This paper categorized the selected factors of this model into three groups after conducting an extensive literature review. This literature review highlights the significance of these factors in relation to digital transformation readiness in the construction phase. Afterwards, Then Delphi analyses was carried to ensure the inclusion of all factors are effecting Digital Transformation in building construction. Therefore, this model used three primary constructs (Technology, Policy and Infrastructure) mentioned in Appendix A.

2.3. Critical Success Factors in the Digital Transformation of Building Construction Activity (Technology)

This section focuses on the technological advancements that have revolutionized construction site activities by improving communication with stakeholders and team collaboration. A variety of equipment and technologies, when applied during the construction stage, can enhance efficiency, on-site execution, safety, reporting, and overall performance. One example is the use of drones to improve the identification of hazards through the creation of aerial maps and high-resolution images [26]. Meanwhile, the use of wearable technology and IoT sensors has enabled the ongoing monitoring of environmental conditions such as temperature, humidity, and air quality, as well as worker safety [27]. Robotics has found applications in excavation, wall spraying, and modular construction [28]. Similarly, refs. [29,30] have demonstrated that 3D printing can be utilized in the fabrication of precast concrete panels, concrete walls, and mortar intended for use in facades and walls. Interoperability has emerged as a prominent subject in lean construction and BIM due to its ability to enhance the exchange of information, collaboration, and data processing [31]. At the same time, the implementation of AI during the construction phase can improve the decision-making process through the facilitation of pattern detection, material and property data analysis, and other improvements [32]. GIS technology is now extensively employed to visualize site conditions, which provides substantial benefits [33]. AR and VR enable remote collaboration, supply up-to-date risk information, and aid in progress monitoring [34]. Integrated platforms and virtual private networks are examples of communication channels that can be protected by cybersecurity protocols [35]. As specific technologies have different benefits, it is important to understand the role of each technology and study its unique relationship with digital transformation. These relationships can be impacted by enabling factors such as policies and proper infrastructure, which will be discussed in the next sections.

2.4. Critical Success Factors in the Digital Transformation of Building Construction Activity (Policy)

This section elaborates on existing academic articles that have discussed the significance of policies and regulations in the digital transformation of the construction industry. The authors of [36] illustrated technological revolutions within organizations, working with 10 construction professionals to understand the required implementation of technology in construction firms. Policies and procedures were identified as the top priority, as ensuring the key implementation of technology through internal guidelines and policies can ensure the swift and successful implementation of technologies in construction firms. Further, the authors mentioned the need for industry professionals to improve their skills and increase their awareness of technological mechanisms. The authors of [37] acknowledged reskilling and upskilling as critical elements of digital transformation within the construction sector, supporting this idea. Most of the experts consulted also mentioned the importance of providing policy incentives, which illustrates how policies, guidelines, technical standards, and stakeholder engagement are affecting technological change [36]. The authors of [38] also concluded that new technologies will never be adopted without support from organizational leaders and employees. The same study further identified nine factors impacting the adoption of new technologies, including insufficient understanding or uncertainty surrounding technological progress, unreliable employment prospects for skilled personnel, and dependence on external talent [39]. Regarding the standardization of construction management procedures, a case study in Ireland [40] explained that standardizing technology adoption in the construction industry requires the definition of technology standards, the training of personnel, the development of policies, the monitoring of performance, and the ongoing refinement of processes. Therefore, policy, regulation, strategy, and stakeholder engagement are among the most important factors in ensuring digital transformation.

2.5. Critical Success Factors in the Digital Transformation of Building Construction Activity (Infrastructure)

This section outlines the infrastructure necessary for the development of digital technologies in construction activities, where infrastructure is a critical factor. To ensure data access across all departments and sectors of an organization, technological implementations require their own infrastructure and connectivity [36]. The authors of [36] also note that the implementation of technology will be constrained in practice in the absence of adequate infrastructure for connectivity, data storage, and communication. The study conducted by [41] provides evidence for the viability of implementing 5G technology to integrate smart buildings and digital facility management in Singapore. The effective operation of digital technologies such as digital twins, BIM, AI, AR, VR, and the IoT requires wireless networks with high bandwidth and low latency. According to [42], the facility digitalization process consists of four critical phases: data sensing, processing, data connection, and data preservation. Data sensing is the process of gathering raw data from the physical world and cleaning them to create information that is meaningful. Data connection is the process of linking processed data with other systems or networks. Data preservation is the process of ensuring that data remain intact and accessible over time. Converting information from the real world into digital formats for the purposes of analysis, monitoring, and decision-making is made possible by these four phases and is essential in the process of digital transformation. Cloud computing is also of critical importance in the migration of organizational data. Such systems include server rooms and data centers, among other local hardware storage facilities. Cloud computing, along with wireless networks and the IoT, is a critical component of the infrastructure necessary for the management of corporate data, according to [43]. Furthermore, to maintain control over the entire process, the control room in construction site must be able to respond promptly to emerging situations to ensure minimal disruptions throughout construction activities. Based on the above, we propose the DTRLIIBC framework, shown in Figure 1 below, to measure digital transformation in the execution phase.

2.6. Previous Research and Established Digital Transformation Readiness Level Models in the Building Construction Industry

This section outlines previous tools developed to measure the digital transformation readiness level in the construction industry. The authors of Ref. [44] proposed a multi-criteria decision-making model that assesses the strategic readiness of firms for the adoption of Construction 4.0 technologies. This model incorporates human, relational, and organizational factors, but it does not account for technological variables. The Technology–Organization–Environment (TOE) framework, meanwhile, provides a comprehensive understanding of the adoption of digital transformation based on technological, organizational, and environmental factors, but it does not account for individual characteristics. The authors of Refs. [45,46] observed that organizational beliefs regarding the acceptance of technology can be substantially influenced by individuals within the organization, particularly at the top and senior management levels. The authors of Ref. [47] highlighted the important role that technology adoption by individuals within an organization has in determining organizational readiness. Furthermore, Refs. [48,49] contended that the technological readiness of construction organizations must be evaluated in the context of both individual and organizational factors.

Thus, there are currently no tools which address all relevant parameters to assess the readiness level of digital transformation in construction activities and provide information to clients, contractors, and consultants. The proposed SEM approach will address this gap and generate guidelines for a digital transformation level indicator in construction activities. Moreover, the proposed SEM will contribute to existing knowledge in the construction sector, specifically in the implementation phase.

2.7. Structural Equation Modeling (SEM)

SEM provides a strong statistical framework for understanding complex effects, enabling the examination of structural relationships and causal mechanisms [50]. The ability of SEM to analyze connections between multiple factors and concepts has proven to be highly valuable in a wide range of fields, including construction management and facility management. The authors of Ref. [51] used partial least squares SEM to examine factors affecting the quality of social infrastructure projects. In addition, Refs. [52,53] utilized SEM in conjunction with fuzzy network analysis to identify pivotal factors contributing to the success of contract performance within the construction industry. Their study sheds light on complex interactions that influence effective contract management. The authors of Ref. [54] used the SEM method with fit indices and standardized paths to investigate how coordination factors affect project performance.

The authors of [55] conducted an extensive examination of the use of SEM in construction research, emphasizing its substantial use in articles published in leading construction journals between 1998 and 2012. In addition, ref. [56] integrated SEM with a fuzzy neural network to create a framework for improving safety management in construction. This study showcased SEM’s ability to effectively represent the relationships between variables in a logistic regression.

According to [57], SEM is distinct from other methodologies because it specifically assesses the connections between constructs. SEM, unlike other linear models, incorporates multiple measurements to represent constructs and takes into account measurement errors, thereby improving the accuracy of the results. SEM facilitates rigorous statistical testing by employing a confirmatory approach that connects theoretical concepts with empirical observations [50]. In the field of construction management and engineering, SEM has proven to be effective in analyzing complex relationships and large sets of data [56]. These characteristics make SEM a highly effective tool for comprehending the process of digital transformation in construction management and providing guidance for important strategic decision-making in this crucial field.

3. A Digital Transformation Readiness SEM Model in Building Construction in the Execution Phase

This article presents the development and execution of an extensive, multidisciplinary assessment framework, based on SEM, for determining the Digital Transformation Readiness Level Index in Building Construction in the execution phase (DTRLIIBC). The DTRLIIBC represents a methodical approach to managing construction activities in the execution phase and enables an understanding of the digitalization readiness required to leverage the benefits of digital technologies to improve services and operations. Unique operational indicators are associated with each construct. A set of twenty indicators includes the responsibilities and accountability of clients, consultants, contractors, subcontractors, and suppliers, improved data utilization for building projects and control, and the provision of user-friendly technologies.

These indicators are broken into three groups. Group 1 is concerned with the implementation of technology in construction during the execution phase, which results in distinct data flow outcomes. Group 2 relates to the policies and guidelines that promote the digital transformation of buildings to ensure proper implementation through technology standards, the training of target people involved in building projects, and stakeholder engagement. Group 3 comprises the requisite infrastructure, comprising hardware and software that guarantee the transmission of data through appropriate channels to ensure that all stakeholders are kept informed during the execution phase. These constructs operate in concert to facilitate the transfer of information and enhance the decision-making process.

4. Research Identification, Methods, and Data Collection and Interpretation

4.1. Research Identification and Methods

As mentioned previously in the literature review, past research has primarily focused on the digital transformation applications and not on the digital transformation readiness level, specifically in relation to building initiatives. Moreover, it identified and resolved the pivotal elements that contribute to its success. To overcome this limitation, the authors consulted a vast array of pertinent scholarly sources to identify the critical success factors for digital transformation within the building construction industry in the execution phase. The authors conducted indicator selection using open-source articles and publications related to digital transformation in the building construction phase. In line with previous research, this study confirms the success factors in the building construction phase by looking at research studies on “construction”, “digital transformation”, “technology applications”, “policy”, “infrastructure”, “construction 4.0”, “digitalized construction”, and “digital construction” in a publication database such as google-scholar, web of science and Scopus [58].

This study conducts a PRISMA assessment to evaluate a propre research methodology to ensure selecting relevant papers matching the research scope. The research method is divided into four phases: The first phase focuses on the identification that focuses on conducting an examination of papers from the publication database from 2008–2024. A total of 850 papers were identified in the first phase. The second phase is about screening articles that were applied to the related articles after reading titles, abstracts and the removal of redundancies. 567 articles were subsequently selected for further consideration. The third phase involves testing the eligibility of relevant articles after an analysis of the content of papers, and the number of articles reached 299, which is in deep focus on digital transformation in the construction phase. The final phase is related to the inclusion process, which entails a comprehensive quantitative and qualitative assessment of papers and thus brings the final number of papers equal to 154.

Based on the literature review, this study adopted a qualitative data collection approach, consulting experts with more than 20 years’ experience to assess potential factors related to digital transformation. In addition, it used a Likert-scale survey to measure the effectiveness of the responses [59].

The survey was distributed to carefully selected respondents, including executive managers, department managers, project managers, senior engineers, and supervisors. The first group of questions in the survey gathered background information about respondents, while the remainder addressed the respondents’ views of the importance of various success factors affecting digital transformation in construction. To ascertain the level of readiness in the construction industry, this study employs a sequential mixed-approach research design, combining qualitative and quantitative methodologies for data collection and evaluation. The subsequent subsections detail each methodological stage. The existing body of research suggests that knowledge regarding the variables that influence digital transformation in the construction sector is presently limited, with prior research concentrating on specific technologies and their implementation within this domain. In addition, there is a scarcity of studies assessing the significance of each element in supporting digital transformation in the construction sector. This underscores the need for further examination.

4.2. Data Collection and Interpretation

After retrieving the data, the authors used a systematic qualitative content analysis approach to identify and categorize the factors impacting success in digital transformation in the construction industry during the construction phase. Following a comprehensive examination, the authors successfully compiled lists of twenty factors that can serve as indicators of a building readiness for digital transformation. The authors classified these indicators into three process groups: technology, policy, and infrastructure.

4.3. The Validation of the Identified Variables

To improve the reliability, validity, and completeness of the compiled lists, a Delphi analysis was conducted with the participation of 13 experts, thereby eliminating any irrelevant or redundant factors. The use of Delphi clearly brought the 13 participants to a mutual consensus on the presented factors [60]. To enhance the reliability, validity, and comprehensiveness of the compiled lists, the authors sought guidance from four specialists with extensive knowledge regarding the technical expertise of the construction industry. The authors conducted individual interviews with the consultants using a semi-structured approach. An extensive overview of this study’s goals was given to the specialists. After the experts gave their consent to participate in this study, they appraised the key success factors that had been identified and the process groups into which they had been categorized. The authors asked the participating experts to comprehensively evaluate the indicators and their categorization and to suggest any possible modifications. The four specialists interviewed, along with each being an expert in digital transformation, represent a wide range of building sectors. Each also possessed a minimum of 15 years of experience in construction management and their construction firms as presented in Appendix B.

4.4. Development and Design of Survey Questionnaire Sent to Experts

4.4.1. The Design

The authors surveyed construction industry experts to determine how each indicator and construct affects the overall effectiveness of the digital transformation readiness level in the building construction phase. The authors checked the list of indicators and constructs and then formulated the survey using a web-based application, SurveyMonkey, which facilitates the creation and dissemination of digital surveys. The authors then disseminated the aforementioned information to construction management experts who specialize in construction practice and digitalization initiatives through various channels, such as SurveyMonkey databases, email, and social media. The survey was split into four distinct sections. The first section delved into the digital transformation taking place in the building construction sector during the execution phase. This part endeavored to evaluate the digital transformation readiness level to gauge the extent of digitalization across all sectors and stakeholders in the construction management industry. The survey was divided into four distinct sections: respondent background information, technology, infrastructure, and policy. Parts 2 to 4 of the survey listed the elements influencing digital transformation and then asked participants to rate these elements on a Likert scale. Through a further analysis of these data, the authors were able to determine the relationship between components’ readiness for digital transformation, impacting the building’s overall readiness level during the execution phase. In addition, demographic information was gathered from the participants, such as the number of years of experience they had in buildings and digitalization, their sector and company affiliations, and their areas of expertise. The third section contained a series of questions regarding the significance of the 20 DTRLIIBC indicators from the three groups. Based on their practical experience, survey respondents were asked to assess the importance of digital transformation in building construction during the execution phase, as well as the significance of various indicators and constructs in determining the overall digital transformation readiness level in a building construction project. The questions posed in this section employed a Likert scale comprising five points, with possible responses ranging from “5” (extremely important) to “1” (not important at all). The measurement instrument employed to quantitatively assess the impact of the indicators and constructs is detailed in

Table 1. The significance scale.

Importance of Factor
Not important at all Slightly important Moderately important Very important Extremely important

and presented in Appendix C.

4.4.2. The Dissemination

Purposive sampling was used in this study to distribute the planned survey questionnaire. This is a popular type of semi-random sampling in which the researcher deliberately chooses respondents at random from a specific population subset. This method was chosen because it allows for the selection of informed and competent participants who can provide high-quality insights which accurately represent the chosen population [61].

Obtaining sample data that accurately represent the complete population of interest to the researcher is the aim of this methodology. The authors of [62] give a summary of the purposive sampling process, which consists of the following stages: the research problem and the specific information required are identified; participants or participant groups are selected; where appropriate participants or participant groups are identified based on the preset criteria; appropriate data collection techniques are implemented; and any potential biases or uncertainties are documented. This study’s participant groups were chosen based on their employment status as professionals in the digitalization sector of the construction industry. To lessen the impact of geographic bias, the survey was sent to a diverse set of specialists from different geographical areas.

4.4.3. The Sample Size Determination

Conducting a thorough analysis is crucial to ensure that the data obtained appropriately represent a diverse range of viewpoints, thereby establishing a strong foundation for further inquiry. At present, there is no agreement on the most effective sample size for SEM. However, according to the research conducted by [63], a sample size under 100 may be considered appropriate for cases with a small number of variables and positive statistical properties. Nevertheless, the authors strongly advise a sample size of at least 200 whenever feasible. In the study conducted by [55], it was found that 77% of the papers reviewed used a sample size of fewer than 200. In our investigation, a total of 215 people completed the survey.

5. The Structural Equation Model

To create a strong structural framework that analyzes the relationships between various constructs and indicators to determine the degree of readiness in the construction industry, this study used a two-step technique. The structural model’s validity and reliability were evaluated using confirmatory factor analysis (CFA), while the structural path and factor loadings were estimated using the bootstrapping maximum likelihood approach [50].

5.1. The Determination and Estimation of the Model

A thorough conceptual model was formed by outlining the connections and associated equations proposed in the model specification. While model estimation refers to the deliberate selection of an appropriate estimating approach for the purpose of identifying the model parameters, model identification ensures the presence of a single numerical solution for the selected model. To determine the relationships between the factors (indicators) in the suggested DTRLIIBC model and the main constructs, conformity factor analysis was used. As shown in Figure 2, the measurement framework consists of 20 indicators arranged into three main latent categories.

The creation of a structural model makes it possible to predict how primary and secondary components will correlate. For the structural framework of the DTRLIIBC in the execution phase, a reflective secondary construct is linked to three fundamental latent constructs, as shown in Figure 3: technology, policy, and infrastructure. The DTRLIIBC model indicates that there is a positive link between the DTRLIIBC and these three first-order constructs. The two main hypotheses were developed based on these presumptions and further divided into smaller hypotheses. The two main hypotheses guiding this investigation are as follows:

• H01: Each of the three components in this study had a positive effect on the DTRLIIBC.
• H02: The three components’ combined effects on the overall digital transformation readiness level in the building construction phase are reflected in the composite model.

5.2. Goodness of Fit (GOF) Indices

The utilization of goodness of fit (GOF) metrics is critical to improve models and determine their ability to assess the underlying constructs of interest. The literature outlines numerous tests that can be utilized to evaluate the fit of a model [64]. However, consensus regarding the optimal tests has yet to be achieved. For this study, an assessment of the model’s alignment with the research objectives was conducted utilizing the relative chi-square (χ²/df), root mean square error of approximation (RMSEA), and comparative fit index (CFI), in accordance with the guidelines provided by [64,65].

The chi-square value (χ²) serves as a measure of the degree of deviation that the observed covariance matrices exhibit from the predicted values [64]. A significant χ² value indicates a substantial deviation between the model and the data. The range of relative chi-square (χ²/df), which is utilized to determine optimal scaling, is from 1 to 3 [55].

The comparative fit index (CFI) assesses the degree to which the proposed model aligns with the empirical data when compared to a reference model. Possible CFI values range from 0 to 1, where a minimal threshold of 0.90 signifies a satisfactory model fit according to the parameters established by [66]. By quantifying the difference between the estimated and actual covariance, the RMSEA places a tangible value on the discrepancies in covariance in comparison to the saturated model. The RMSEA is a metric utilized to assess the degree of model deviance, and [50] specifies that an acceptable range is between 0.05 and 0.1.

However, ref. [66] argues that a value below 0.08 is generally regarded as indicative of a satisfactory degree of fit. The author of [67] suggests that indicators with factor loadings below the critical threshold of 0.40 ought to be omitted from the refined model. This is due to the fact that such indicators, which have a low correlation with the corresponding constructs, have the potential to compromise the model’s integrity. One variable in the model had a factor loading value below this threshold: G1.03 (utilization of laser scanning in construction). This variable was thus omitted from the DTRLIIBC model, and the indices were adjusted, as illustrated in Figure 4. As shown in

Table 2. Goodness of fit indices for the modified measurement model.

Number	Model Fitness Index Symbol	Default Model Estimate	Limit	Goodness of Fit
1	Chi-square (χ2)	465.353		-
2	Degree of Freedom (DF)	167		-
3	Pcmin (χ2/DF)	2.787	Between 1 and 3	Well-Fitting
4	CFI	0.902	>0.90	Well-Fitting
5	GFI	0.817	Between 0 and 1	Acceptable
6	AGFI	0.770	Between 0 and 1	Acceptable
7	RMR	0.024	>0.08	Well-Fitting
8	RMSEA	0.077	>0.08	Well-Fitting

Note: GFI—goodness of fit index; AGFI—Adjusted Goodness Of Fit Index; RMR—Root Mean Square Residual; RMSEA—root mean square error of approximation.

, the adjusted measurement model’s fitness is validated by the GOF indices. The computed value of χ²/DF (2.787) is below the threshold of 3.00 suggested by [67,68]. The degree of discrepancy between the hypothesized model and the observed data is evaluated by the GFI in SEM, which has a value of 0.817, which is within the acceptable value range of 0 to 1 as suggested by [69,70]. The Adjusted Goodness Of Fit Index (AGFI) value of 0.770 is likewise within the acceptable range of 0 to 1 as suggested by [71,72,73].

The RMR value of 0.024 is below the threshold value of 0.08 as suggested by [64]. The respective RMSEA value is 0.077, which is within the acceptable range as defined by [74].

5.3. The Reliability and Validity of the Measurement Model

After determining the appropriate estimation method, it is critical to assess the model’s validity and reliability prior to presenting the structural model. As stated in [56], validating the construct is an essential element in assessing the dependability of a model and strengthening its theoretical underpinnings. The assessment of reliability entails the utilization of Cronbach’s alpha to determine the consistency of constructs.

Convergent and discriminant validity are also assessed as part of the procedure for determining validity. The assessment of the questionnaire’s indicators’ reliability was conducted using the SPSS 29 statistics software package in conjunction with the Cronbach’s alpha test. This test ascertains the measurement’s coherence using a minimum threshold value of 0.7 [66]. The Standard Factor Loading (SFL) method was utilized to conduct a unidimensional assessment. As shown in Figure 4, the SFLs of all indicators exceeded the threshold of 0.40 defined in [67] and had positive values. Therefore, the digital transformation readiness level index model successfully attained the criterion of unidimensionality. The results of this examination are illustrated in

Table 3. Latent variable values both in Cronbach’s alpha and composite reliability.

Number	Groups	Cronbach’s Alpha	Composite Reliability (CR)
1	Technology	0.887	0.941
2	Policy	0.892	0.937
3	Infrastructure	0.899	0.942

.

5.4. Testing Convergent Validity

Convergent validity (CV) describes the extent to which multiple elements of a specific construct, which are hypothesized to be interconnected according to theoretical frameworks, in fact exhibit interconnection [75]. Based on a study conducted by [67], a satisfactory CV is defined as one in which the composite reliability (CR) exceeds 0.7 and all SFLs of a construct exceed 0.40. In order to calculate CR, the sum of the factor loadings for each construct (Li) is squared, and the total error variance terms of said constructs (ei) are also accounted for.

$$CR={\displaystyle \frac{({\sum }_{i=1}^{n}L{i)}^2}{({\sum }_{i=1}^{n}L{i)}^2+{\sum }_{i=1}^{n}ei}}$$

(1)

Equation (1) defines “Li” as the SFL, where “i” and “n” represent each item in a set and the total number of items, respectively. Furthermore, the error variance in construct “i” is denoted by “ei”. According to [76], adequate convergent validity can be ascertained through the use of CR alone. Additionally, as shown in Figure 4, all SFL values exceeded the threshold value of 0.40, confirming acceptable convergent validity in accordance with [77]. These results support the conclusion that the construct demonstrates a substantial level of internal consistency, and the model can be considered extremely dependable, according to [76]. Furthermore, the calculated values for SFL and CR serve as strong indicators that the requirements for convergent validity have been fulfilled.

5.5. Establish DTRLIIBC Structural Equation Model

An SEM approach was used to develop a conceptual framework that illustrates the direct impact of each build on the DTRLIIBC during the execution phase. Figure 5 depicts the impact of the primary construct on each component in the structural model.

Table 4. Goodness of fit indices for the modified measurement model.

Number	Model Fitness Index Symbol	Default Model Estimate	Limit	Goodness of Fit
1	Chi-square (χ2)	437.516		-
2	Degree of Freedom (DF)	149		-
3	Pcmin (χ2/DF)	2.936	Between 1 and 3	Well-Fitting
4	CFI	0.902	>0.90	Well-Fitting
5	GFI	0.817	Between 0 and 1	Acceptable
6	AGFI	0.767	Between 0 and 1	Acceptable
7	RMR	0.025	>0.08	Well-Fitting
8	RMSEA	0.074	>0.08	Well-Fitting

Note: GFI—goodness of fit index; AGFI—Adjusted Goodness-Of-Fit Index; RMR—Root Mean Square Residual; RMSEA—root mean square error of approximation.

presents an overview of the results obtained from fitting the proposed structural model. The findings show that all goodness of fit (GOF) indices were successfully satisfied, suggesting that the structural model satisfies the requirements for a favorable fit.

Once the model GOF was determined to be acceptable, the variance explained figure (R²) was used to assess the significance of the correlation between the DTRLIIBC model and the second-order constructs. According to [55,66], R² should be greater than 0.50. All the SFL values exceeded the threshold value of 0.4, according to the data presented in

Table 5. Construct groups representing SFL and R2.

Number	Groups	Standard Factor Loading (SFL)	Variance Explained (R2)
1	Technology	0.820	0.672
2	Policy	0.920	0.846
3	Infrastructure	0.870	0.757

. These results suggest a strong relationship between the DTRLIIBC and the modified constructs.

6. Discussion and Analysis of Results

In this study, a model for determining the digital transformation readiness level was developed, producing the DTRLIIBC. This model takes into consideration a wide range of construction-related characteristics and components in building projects. SEM was used to quantify and rank the significance of several global digital transformation success indicators and constructs according to their SFL. The results confirm that the execution of building projects in the construction sector has a significant and positive impact on the process of digital transformation. Furthermore, the indicators of the readiness level have a positive impact on these building projects, which is another positive influence. The purpose of this section is to offer a brief summary of the data that were acquired and the analysis that was carried out.

6.1. The Data Survey Validation

In order to analyze the influence of each component and indicator on the overall digital transformation readiness level based on the linguistic components that were introduced, a survey was sent electronically to a broad range of industry specialists working in a range of national contexts. The participants included executive managers, department managers, project managers, senior engineers, and supervisors. The present investigation thus involved a diverse group of persons with significant knowledge and experience regarding various building projects.

As mentioned in the Methodology Section, the survey was sent to potential respondents via various social media outlets and email. It was sent to 611 respondents, and a total of 382 responses were gathered. A total of 215 of the responses were deemed complete and valid, while the remaining 164 were eliminated due to the fact that they were either incomplete or outliers. The rate of usable responses was thus 35.6%, which is comparable to the average response rate to an online survey, which is 34%, as reported by [76,78].

This also exceeds the typical response rates of 10 to 15% that [79] recorded, as well as the 22.9% found by [80]. Furthermore, a variety of stakeholders in the digital transformation of the building construction industry are represented by the survey data, which enhances the data’s utility for the purposes of this study.

6.2. Respondent Demographics

An illustration of the classification of the survey participants according to their years of experience in buildings and digitization, sector affiliation, and area of expertise can be found in Figure 4. A total of 46% of the respondents held management positions within their respective companies, including executive manager, department manager, and project manager positions.

The remaining 54% of the participants held technical positions such as senior engineers, engineers/supervisors, and quantity surveyors. The majority of the respondents were working in client, consultant, or contractor firms, with these three types of organizations accounting for 19%, 27%, and 38% of the sample, respectively.

A total of 10% of the remaining respondents work for firms operating as subcontractors and 6% as suppliers (Figure 6a). A breakdown of participants’ areas of expertise and job titles is provided in Figure 6b,c. The participants were distributed among the public, private, and semi-government sectors at 21%, 61%, and 18%, respectively.

6.3. Constructs’ Ranking Comparisons amongst the Respondents

A Relative Importance Index (RII) test was applied to the data collected from the survey responses to assess and rank the degree of relevance for each construct, according to specialists from a variety of different types of organizations and sectors. The authors also compared the rankings of each group of respondents to assess the various perspectives on digital transformation in construction management, particularly within building construction projects. The RII was determined according to the following formula:

$$\mathrm{R}\mathrm{I}\mathrm{I}={\sum }_{\mathrm{i}=0}^5{\displaystyle \frac{\mathrm{w}\mathrm{i}\ast \mathrm{X}\mathrm{i}}{\mathrm{A}\ast \mathrm{N}}}$$

where W is the weighting that the respondents applied to each construct (ranging from 1 to 5), X is the frequency of responses that were provided for each W, A is the greatest weight that was applied (5), and N is the total number of participants 215 for all survey participants. The possible values for the RII score range from 0 to 1, with a higher number suggesting that the construct in question is more relevant than others. The RII values for each construct are shown in

Table 6. RII values based on importance of each group.

Group Name	RII	Rank
Policy	0.842	1
Infrastructure	0.829	2
Technology	0.805	3

below and show that policy and infrastructure are slightly more important than technology. This is likely because most participants were primarily concerned about the need for policy and training.

Ranking of Constructs amongst All Respondents

This section presents an analysis and comparison of several different businesses. This analysis takes into consideration aspects such as the business models and operational models of the organizations and evaluates the relative influence of these models. The hierarchical relevance of ordering groups is shown in

Table 7. The ranking of all the score values per the involved stakeholders.

	Client			Consultant			Contractor			Subcontractor			Supplier
Factors	Rank	AR	GR	Rank	AR	GR	Rank	AR	GR	Rank	AR	GR	Rank	AR	GR
G01.01	17	-	-	14	-	-	17	-	-	13	-	-	14	-	-
G01.02	16	-	-	12	-	-	6	-	-	16	-	-	3	-	-
G01.03	1	-	-	3	-	-	1	-	-	1	-	-	5	-	-
G01.04	19	-	-	15	-	-	14	-	-	4	-	-	12	-	-
G01.05	18	-	-	18	-	-	19	-	-	19	-	-	15	-	-
G01.06	12	-	-	8	-	-	3	-	-	5	-	-	2	-	-
G01.07	5	-	-	6	-	-	2	-	-	2	-	-	1	-	-
G01.08	9	-	-	19	-	-	18	-	-	15	-	-	6	-	-
G01.09	13	-	-	16	-	-	15	-	-	18	-	-	19	-	-
G01.10	2	-	-	9	-	-	4	-	-	3	-	-	4	-	-
G01.11	14	-	-	17	-	-	16	-	-	17	-	-	13	-	-
G01.12	3	-	-	13	-	-	11	-	-	9	-	-	10	-	-
G01.13	20	11.5	3	20	13.1	3	20	11.2	2	20	10.9	3	20	9.53	1
G02.01	11	-	-	2	-	-	7	-	-	10	-	-	7	-	-
G02.02	6	-	-	5	-	-	10	-	-	14	-	-	8	-	-
G02.03	15	-	-	4	-	-	8	-	-	6	-	-	17	-	-
G02.04	7	9.75	2	1	3.0	1	5	7.50	1	7	9.25	1	9	10.3	2
G03.01	8	-	-	11	-	-	13	-	-	8	-	-	18	-	-
G03.02	4	-	-	7	-	-	9	-	-	11	-	-	16	-	-
G03.03	10	7.33	1	10	9.3	2	12	11.3	3	12	10.3	2	11	15.0	3

Note: AR—Average Rank; GR—Group Rank.

, organized according to the following five stakeholders: the client, the contractor, the consultant, the supplier, and the subcontractor.

Among each group, factors are ranked from the most important (1) to the least important (20). These rankings were determined based on the average survey responses for each factor. The group rankings were also calculated for technology (factors G01.01–G01.13), policy (factors G02.01–G02.04), and infrastructure (G03.01–G03.03).

The factor and factor group ranking values are shown in Table 7. Unlike contractors, clients and consultants are primarily concerned with the impact of policy on digital transformation; contractors are more concerned with infrastructure and technology. Consultants, subcontractors, and suppliers, meanwhile, all value the development of solid infrastructure, but consultants are particularly concerned with the transfer of data without any interruptions.

The Relative Importance Index (RII) was computed using the survey response data to determine the importance of each construct as assessed by experts from a variety of professions and organizations. The RII was employed to quantitatively evaluate the significance levels in this study. Additionally, the RII was employed to compare the evaluations obtained from various respondent groups to facilitate an analysis of a variety of perspectives on the constructs associated with digital transformation during the construction phase.

7. An Analysis of the SEM Results

Based on the construct-level analysis, it can observed that the policy group has the highest SFL for the digital transformation readiness level in the building construction industry in the construction phase, at 0.92, compared to infrastructure and technology, at 0.87 and 0.82, respectively. The research thus shows that the current lack of regulations and standards has a significant impact on the implementation of digital transformation. This highlights the significance of regulations and standards in the effective adoption of digital technologies in building projects. Training sessions are essential in the construction business to tackle the shortage of skills and facilitate digital transformation as they facilitate the identification of skill gaps and the adoption of new technology, hence improving productivity and the transfer of information. Employers should provide training programs tailored to the needs of specific positions and departments, along with continuous assistance and resources, as mentioned by [59]. Training sessions focused on upskilling and reskilling may help create a workforce that is prepared for the future, provide a framework for lifelong learning, and support wider skill development in the construction sector [39,81,82].

In the policy group, G2.02 (engagement of stakeholders on digital transformation during construction) has an SFL of 0.90. However, other factors that affect policy, such as the implementation of technology standard procedure in construction, the establishment of digital transformation workforce on organizational structure in the project, and the utilization of technology awareness sessions and training sessions for construction project teams in construction, with SFL of 0.85, 0.79 and 0.74. In addition, stakeholder engagement has the highest score compared to other factors. According to [83], stakeholder engagement in construction projects enhances collaboration, inclusion of diverse viewpoints, and joint action on common concerns. It also contributes to change management and technological know-how, and is crucial for achieving Construction 4.0 goals, BIM has facilitated successful stakeholder engagement in Malaysia, improving decision-making, planning, and resource management, as per [84,85].

The infrastructure group is the second-most important, with an SFL of 0.87. Within this group, G3.03 (Establishment of a control center for construction works) has the highest SFL at 0.90, while establishment of storage and data access infrastructure to support common data environment, other information management processes, and the establishment of an effective communication network and protocol have SFLs of 0.81 and 0.88. To achieve digital transition in the construction industry, strong and reliable infrastructure is necessary, which encompasses IT support and cloud computing. This infrastructure is essential for efficient project management, communication, and data retrieval. The process consists of four fundamental steps: detecting, connecting, storing, and processing data. Cloud computing is crucial for the management of business data, although local hardware storage solutions are also vital. A smart site requires the integration of communication, storage, and IoT devices, with cloud computing playing a vital role. Setting up a control center for construction work is crucial for the success of a project. The on-site deployment of digital technologies requiresd fast and reliable connections with low latency and high bandwidth, as well as wireless networks, as mentioned in [43,86,87,88,89].

Finally, the Technology group is the least important according to the survey findings, with an SFL 0.82. The construction industry is being revolutionized by digital transformation, which involves the integration of advanced technologies such as BIM, cloud-based project management, drones, 3D laser scanning, IoT sensors, AR and VR. BIM software enhances design and cost estimations, minimizes mistakes, and optimizes scheduling. Drones and 3D laser scanning provide data for the purposes of building design and construction planning. AR and VR technologies replicate the construction process, enabling stakeholders to get a deeper comprehension of building designs. Robotics and automation enhance the efficiency and effectiveness of equipment operations, ensuring safety and improving the work environment. Digital twins enhance safety by using IoT devices to monitor various parameters like as temperature, humidity, air quality, and worker safety. AI optimizes company operations and service processes, while blockchain technology promotes contract administration, cost control, and transparency. GIS performs the analysis of spatial data, while cybersecurity aids in the identification of risks and vulnerabilities. The model indices demonstrate that technologies like Building Information Modeling (BIM) have a Strategic Fit Level (SFL) of 0.66, highlighting the essential importance of BIM in the construction phase. BIM is widely employed during this stage as it integrates data into a cohesive environment, eliminating barriers and improving data accessibility for the entire project team. By using specialized BIM technologies, data can be consistently updated, which streamlines the handover process and enhances operational efficiency. This eventually ensures a smooth transfer of the building to the final recipient. Thus, BIM is one of the most reliable tools in the construction phase. The integration of BIM with 3D laser scanning enhances the level of quality control in construction [58].

The results of the structural equation modeling (SEM) analysis have led us to the conclusion that have effectively accomplished the purpose of the study. The authors achieved this by identifying the critical success factors that impact the construction phase of the digital transformation. Furthermore, an SEM model was established to assist construction project directors in analyzing their companies.

8. Conclusions

Digital transformation in the execution phase has the potential to generate significant value for the construction industry and should therefore be afforded high priority. It is imperative that clients, consultants, contractors, subcontractors, and suppliers act quickly to enhance digital readiness in the industry. This is because traditional methods of monitoring data and assets are rapidly losing their relevance. Previous research has left the construction industry and its organizations uncertain as to how to successfully execute digital transformation and achieve digitalization. The construction industry’s lack of R&D has contributed to this uncertainty. Moreover, during the execution phase of construction projects specifically, there is a lack of adequate policy and training. To support stakeholders in addressing this gap, the current research presents a comprehensive digital transformation readiness assessment tool comprising twenty indicators, categorized into three areas: technology, policy, and infrastructure. This tool’s purpose is to determine the readiness level for digital transformation in building construction during the execution phase. We made use of structural equation modeling (SEM) to analyze factors that make up the digital transformation readiness level index, allowing for an evaluation of the current level of digitalization in the industry and the consequent effects this has on organizations and the construction industry as a whole. Using structural equation modeling and SPSS AMOS V26, 215 industry experts were surveyed concerning the relevance of the indicators included in the model. The results shed light on the considerable effect that key performance indicators have on the digital transformation of the building construction process during the execution phase. This is particularly true in the areas of technology adoption, effective policy, and strong infrastructure, which together serve to enable seamless data movement across multiple systems and collection in a single data environment. In addition, this is especially clear in the area of technology adoption.

Based on the survey findings, the authors propose the Digital Transformation Readiness Index Level Index in Building Construction (DTRLIIBC) in the execution phase measurement model as a quantitative instrument for planning, monitoring, and evaluating digital readiness within the construction industry and its organizations. This model aims to provide useful insights that can guide the development of improvement initiatives. The indicators in the model will facilitate the execution and evolution of digitalization projects for companies in the construction industry. This research thus contributes to the execution phase of building construction by providing a pragmatic framework for digitalization assessments. This framework was developed with the aim of addressing the specific deficiencies currently present in the industry, as identified in the literature review. In conclusion, this work offers a foundation for future research, enabling future researchers to identify bottlenecks and address concerns related to the implementation of digital transformation in building construction during the execution phase, using a variety of methods to quantify their findings. This study aimed to emphasize the importance of factors that impact digital transformation in the construction phase of vertical construction projects. The results demonstrate and include emerging digitalization trends, demonstrating significant overall advancement towards digital transformation in building construction activities, hence supporting the prior hypotheses that were mentioned. The model indices and standard loading factors confirm the precision and reliability of the model analysis hypotheses. The first hypothesis was focusing on significant relationships detected on each of the three primary constructs and the DTRLIIBC. The second hypothesis, which states that the incorporation of these three elements improves the digital transformation readiness level index in the construction phase, has been verified. This article’s scope specifically examines its model in building construction and proposes the future work for applying comparable approaches to horizontal projects, such as infrastructure systems, energy, water supply, and road networks. This method will streamline the incorporation of emerging digitalization trends, resulting in substantial overall advancement. Subsequent research could enhance this approach by comparing the construction phases in vertical and horizontal projects in more depth.