Unveiling Digital Transformation: Analyzing Building Facility Management’s Preparedness for Transformation Using Structural Equation Modeling

Abstract

Digital transformation (DT) is revolutionizing building facility management (FM) by streamlining operations, maximizing resources, and increasing performance. FM firms prioritize DT to stay competitive as speed and efficiency become more crucial in the corporate context. Traditional FM methods like manual record-keeping and reactive maintenance cannot meet recent corporate needs. Modern technologies such as IoT, AI, digital twins, and data analytics enable real-time building asset monitoring and optimization. This paper evaluates the digital transformation readiness level index of building facility management (DTRLIBFM), which includes critical success factors for DT development, using structural equation modeling (SEM). DTRLIBFM model determines the importance of key factors that contribute to the success of digital transformation initiatives, specifically focusing on the DT readiness level index of building facility management. Expert interviews and an intensive literature review were employed to identify the critical success factors for the DTRLIFMB. The Delphi technique was employed to validate these factors with 13 highly experienced professionals, and they were subsequently appraised for significance through an online questionnaire that was circulated to industry experts. To quantitatively evaluate the interconnectedness of different elements of DTRLIFMB and establish the impact of each construct on the overall digital transformation, data from 220 participants from around the world were analyzed using (SEM). The evaluation included reliability, validity, hypothesis testing, multivariate normality, and goodness-of-fit indexes. The DTRLIBFM model, with 20 indicators in three constructs, explains how DT readiness affects a building’s digitalization importance level. The research provides governments, organizations, contractors, consultants, suppliers, subcontractors, and facility managers with a current DT index and DT roadmap for building facility management.

1. Introduction

While the concept of digital transformation (DT) is still evolving, a consensus on the definition of facility management (FM) appears to have been reached in the literature. Generally, FM denotes the function of managing a building through the integration of key stakeholders and components of the built environment [1]. According to the IFMA (International Facility Management Association), it integrates place, people, process, and technology. It aids in improving the performance of organizational processes as well as the quality of life of the occupants. It may also be viewed as the function of integrating multiple disciplines to ensure the incorporation of stakeholders into the built environment with the goal of ensuring the general performance of a facility [2]. One of the most important functions of FM is supporting occupants in their core business processes. The FM phase comprises most of the lifecycle of a building in the construction industry [3].

Moreover, the management of building facilities continues to depend on conventional approaches rather than emerging technologies. The concept of DT in FM is gaining more attention as an essential factor in obtaining high organizational performance. Considering the ongoing shift towards digitalization on a global scale, various international sectors, including government, hospitality, healthcare, manufacturing, education, real estate, and more, are taking the lead in DT initiatives. For example, authors of [4] undertook an exhaustive review of the literature concerning digital technologies in the context of FM, defining their prospective influence on the overall performance of buildings. An analysis was conducted on various technologies, including 3D laser scanning, geographic information systems (GIS), building information modeling (BIM), and the Internet of Things (IoT). The assessment highlighted the major effect that digitalization could have on the field of FM and emphasized the importance of future research concentrating on critical domains.

The proposed DT developments in buildings encompass enhancing data interoperability, optimizing the generation of BIM/GIS asset databases to facilitate more effective facility management, and enhancing the accuracy of point cloud data to enable the development of precise as-built models for existing facilities. The authors in [5] assert that these innovative ideas span a wide range of fields, such as the digital progress of the facility. Using structural Equation modeling (SEM), this research paper presents an advanced method to assess whether FM is prepared for DT. Despite the growing focus on digitalization in the construction industry, existing research lacks comprehensive readiness assessment tools specifically designed for the field of FM in buildings. Using SEM as the analytical framework, the proposed methodology intends to address this gap by analyzing the effect of various factors on the DT readiness level in the building facility management phase. This includes consideration of the importance of implementing policies and proper infrastructure to support DT in building facility management [6].

This study improves the current understanding of and relationship between building facility management and DT implementation. This study will identify success criteria for DT, including the readiness level index of building facility management and a broader range of best practices for DT methodologies. Moreover, the scientific research verifies that implementing the digital transformation readiness level index of facility management model (DTRLIBFM) will have a beneficial impact on the entire digitalization of buildings as well as the buildings’ overall performance and assets. Verification is achieved through the establishment of a global-scale structural equation model (SEM) that efficiently assesses DT in building facility management by evaluating crucial success factors. The model oversees DT domains and facilitates the creation of programs to boost digitization in buildings, motivating stakeholders to enhance their DT initiatives in the FM field. Likewise, professionals in the industry can utilize the suggested DTRLIBFM to assess the extent of readiness for DT and determine whether their companies need to review their digitalization readiness levels. Moreover, the model can serve as a benchmark for assessing processes across the DT roadmap to pinpoint the area that needs a focus on digitalization initiatives in a building facility management system. As a result, the innovative assessment framework for the DT readiness level will provide recommendations for where the organization needs to embark on DT initiatives, which, if developed, will subsequently enhance the performance of FM. The significance of this study lies in its combination of an extensive number of indicators and constructs to represent the global view of DT readiness at the operational level in building facility management. Additionally, it establishes an SEM to study the relationships between the factors impacting DT readiness measurement. To the authors’ knowledge, this research is the first to employ SEM in modeling the DT readiness level index in building facility management, thus addressing a significant gap in the current body of knowledge.

2. Literature Review

2.1. The Principle of Digital Transformation in the Construction Industry in the Facility Management Phase

This section explains the significance of the DT concept in building facility management within the construction industry and explores its successful implementation. The potential of DT in the construction industry has recently been boosted as many sectors have moved towards digitalization. Reference [7] highlighted the significance of the construction industry’s capacity to transform and better utilize Industry 4.0 technologies, such as BIM, drones, digital twins, augmented reality (AR), virtual reality (VR), and artificial intelligence (AI), will shape the future of the construction industry. The author also mentioned potential challenges to the implementation of digital technologies, which will be discussed further in the upcoming section of the present study. In the built environment, the use of emerging technologies is still in its infancy. Deep engagement with FM is a crucial component of any organization’s strategy for ensuring business continuity and maximizing investments to boost the organization’s overall success. Reference [8] stated that the COVID-19 pandemic prompted a significant change in the way organizations operate, compelling organizations in all industries to aggressively adopt DT. This highlights the cultural shift toward digitization in buildings and data availability in common data environments. The present section explicates the outcomes of research in three domains that pertain to the potential of DT in the construction industry as a holistic approach, identifies potential indicators of DT in the FM phase, identifies obstacles, and discusses the existing situation regarding the implementation of a digitalization measurement tool for FM in the construction industry.

This literature review demonstrates that academic and research communities are increasingly interested in investigating DT in the construction sector. For example, a systematic literature review was conducted by [9], focusing on a framework that addresses the rapid growth of digital technologies in the construction sector. Integrated decision systems, BIM, the IoT, AR, VR, and cloud computing are the principal digital technologies found to be underutilized. Moreover, it is critical to recognize that the existing body of research concerning technological preparedness in the construction industry is incompatible with a primary emphasis on the implementation of DT technologies, namely, blockchain, BIM, 3D printing, and big data [10,11,12]. The cyber–physical system is also gaining importance for implementing digitalization in the building construction industry in different phases, including FM; this illustrates the significance of potential integration opportunities for improvement of the overall building lifecycle [13].

Moreover, construction stakeholders can precisely recognize and anticipate potential obstacles in the design and construction processes by strategically utilizing DT. As a result, operations are optimized, and project results are enhanced. In addition, this technological advancement promotes a collaborative environment that unites various stakeholders, enabling mutually beneficial exchanges and a more unified incorporation of human resources, operational processes, and environmental factors into the built environment. Authors of [14] conducted an extensive analysis of DT in the construction industry. The author clarified the crucial connection between technological advancements and the necessity for a reevaluation of the industry’s policies and infrastructure. In doing so, the author ultimately outlined a path towards a complete DT and the use of digital technologies in various project phases. This provides an indication of how DT is changing the entire construction industry.

2.2. Critical Success Factors in the Digital Transformation of Building Facility Management (Technology)

This section describes an extensive variety of technologies that play pivotal roles in enhancing DT functions in building facility management. There are still technologies that have not been put into practice. As suggested, the most significant technology in terms of FM, BIM, appears to be a crucial element of DT in FM. Data pertaining to existing buildings was gathered by [15] via the implementation of BIM technology. The acquisition of data was efficiently executed by employing BIM in conjunction with 3D laser scanning, unmanned aerial vehicle (UAV) photography, and computer-aided design (CAD) drawings. This feature enables the seamless integration of data management systems such as computer-assisted facility management systems and maintenance management systems. Another study conducted by [16] shows that the collaboration between BIM and digital twins enhances energy efficiency and FM operations. Additionally, by generating precise data for physical facilities, cloud-based digital twins enable well-informed decision-making when performing daily operational activities [17]. Such varieties of integration exist as FM transitions to an institutional level. For instance, the IoT improves user interaction with the built environment via intelligent digital interfaces that enable accurate data acquisition and transmission for construction resources. Radio frequency identification (RFID) is an indispensable IoT enabler [18]. Additive manufacturing improves the damaged parts of an existing building by customizing them and saves time in terms of requesting spare parts [19]. Furthermore, the use of drones facilitates maintenance protocols by capturing videos and high-resolution images of building components [19]. Robots also are utilized in a wide range of facility management tasks [20], including but not limited to exterior removal, building assessment, cleaning, painting, and fire prevention. Digital twin technology’s sensing and actuation capabilities are improved by AI, while smart contracts for facility control and repair administration are enabled by blockchain technology [21]. FM can employ GIS for environmental monitoring, safety surveillance, and planning [1]. AR and VR have revolutionized FM by enabling the visualization of real-time asset monitoring in structures [22]. It is critical to guarantee the precision and practicality of data, which necessitates the resolution of cybersecurity issues [23]. Photogrammetry and 3D laser scanning are indispensable for precisely documenting parameters within structures; the combination of data from UAV photogrammetry and laser scanning provides comprehensive 3D representation [24]. All these technologies are successful examples of the implementation of digitalization in the management of buildings and facilities.

Due to technological advancement, FM departments and functions have become intelligent and cohesive. Figure 1 below showcases the digital technologies that are consequently available for managing smart building facilities. Earlier, collaboration in FM was defined as a method of integrating technology, processes, people, and places, as indicated and explained in the individual cases of these technology applications. The lifecycle common data environment (CDE) is a component of BIM that integrates a variety of unconnected systems and workflows, thereby enabling a seamless handover process between all participants in a facility’s lifecycle, including architects, engineers, contractors, operators, facility managers, commissioning agents, and other individuals who require information about the facility. Figure 1 illustrates how the integrated approach ensures that all stakeholders have access to precise, current information in the buildings.

Taking FM into consideration is essential in this ecosystem, as it supervises a diverse array of functions to guarantee the efficient and effective operation of buildings. These functions include maintenance, energy, security, assets, safety, security, cleaning, janitorial, and occupation management. Facility managers can improve coordination, streamline operations, and improve data accuracy by incorporating these functions into a CDE as part of BIM. This ultimately results in more efficient building management and better service delivery to occupants, while also reducing the risk of any failure in building facility management.

2.3. Critical Success Factors in the Digital Transformation of Building Facility Management (Policy)

Within the context of DT, this section examines the significance of building facility management policies and organizational procedures that support DT, such as providing a strategy to support digitalization in the FM domain, establishing clear specifications and policies, and investing in training human capital to learn about digital technologies.

The integration of diverse systems, such as CMMS, CAFM, BIM, and BMS, is dependent on technical specifications and standards which ensure the optimal functioning and upkeep of building components. According to [25], digital construction and FM require specific standards to ensure efficient planning and development. The authors illustrated the lack of technical standards and guidelines for the application services of industrial park digitalization by providing a technical framework related to the FM of a digital industrial park, such as complying with building automation management, building geoinformation, the layout of facilities, and a security control system that meets the required standards. However, the digitalization of construction and FM services is limited by a lack of governing standards and regulations [25]. Another relevant topic is reskilling and upskilling, which acknowledged as critical elements of DT within the construction sector [26]. Reference [27] argued that contemporary technological advancements have introduced a new set of competencies that are necessary for effective FM. The need for a holistic transformation of education, skills development, awareness, and competencies was suggested by [28]. To define essential competencies and ensure a strategic congruence between DT and the labor force, governmental intervention is required. Moreover, qualitative research has revealed the enormous potential of implementing appropriate strategies and policies and ensuring the well-being of organizations via corporate digitalization in the construction and FM industries [29]. In furtherance of this assertion, another study suggested that organizations should establish enhanced initiatives that elevate their level of preparedness for the integration of novel technologies [30]. Furthermore, ref. [31] emphasized that the policies implemented within an organization serve as a set of guidelines for its members, establishing a broadly applicable standard. The success of the adoption of new technologies can be enhanced through the implementation of appropriate policies, procedures, and practices designed around them.

2.4. Critical Success Factors in the Digital Transformation of Building Facility Management (Infrastructure)

This section pertains to the implementation of the necessary infrastructure for building FM, which is essential for the advancement of DT. A higher priority is given to components that facilitate the administration, connectivity, and storage of building data. The importance of a robust communication network was discussed in the context of digitization by [32,33]. They stressed that putting vital communication infrastructure in place is essential to an organization’s ability to successfully execute DT. Reference [34] carried out studies showing how 5G technology is used in Singapore to integrate smart buildings and digitalized facilities’ management. Wireless networks with high bandwidth and low latency are required for digital technologies like digital twins, BIM, AI, AR, VR, and IoT to function properly [34].

Four critical phases in the process of digitalizing facilities were outlined by [5], namely, sensing, connecting, preserving, and processing data. The authors placed considerable importance on connectivity as it pertains to the transfer of data. Effective management of organizational data requires the integration of cloud computing with IoT and wireless networks, according to [35,36]. For example, the DT of buildings is facilitated by Cisco’s framework, which emphasizes communication, the use of IoT sensors, and appropriate storage channels.

2.5. Technical Challenges

While DT offers attractive solutions and opportunities for enhancing FM, it faces numerous challenges that have derailed its full adoption. First, some digital technologies may develop security challenges in the context of FM. The automation of building management increases the risk of cyberattacks [37]. In addition to security threats, DTs in building management are associated with interoperability issues due to the increasing diversity of software, IoT devices, and protocols used in the sector. Some systems and applications may fail to integrate with other software and IoT devices. Moreover, existing digital solutions still lack the capacity to capture all building information, particularly those contained inside structural elements [24].

The major issue associated with technological infrastructure in FM is the huge energy consumption demanded for data storage [38,39]. It was mentioned in 2014 that data centers consumed about 1.8% of the total electricity produced in the US. Besides this energy demand, there is a lack of customized wireless networks for various applications required in FM. Furthermore, the integration of BIM with FM requires high-bandwidth cloud computing infrastructure, which is currently lacking in most of the existing networks [35]. Reference [6] mentioned that one primary obstacle encountered during the integration of state-of-the-art technologies into existing infrastructure is the complex nature of such integration.

The integration of legacy FM systems with cutting-edge technologies becomes a formidable obstacle that requires a unique resolution strategy. However, the concept of cybersecurity remains a significant concern in the age of intelligent facilities, as highlighted by [40], who underlined the interdependence of IoT devices and AI-powered systems, thereby emphasizing the susceptibilities that could be exploited by malicious actors.

2.6. Non-Technical Challenges

In addition, non-technical challenges have derailed the adoption and implementation of digital solutions in FM. A lack of adequate legal and compliance mechanisms and regulations in some jurisdictions for recent technologies such as blockchain has also derailed the adoption of digital solutions in smart cities [36]. Another set of challenges for the adoption of digital technologies in FM relate to human capital and can be categorized into resistance to change among users and a lack of technical understanding [6]. People generally demonstrate change-resistant attitudes when introduced to new ways of planning and conducting FM tasks [2]. A further primary category of non-technical challenges involves implementation costs, in connection with which ref. [6] highlighted budget limitations, poor financial planning, and investment prioritization. Most FM activities involve support services and functions and thus do not generate profit; thus, the high cost of digital solutions may hinder their full adoption in FM.

2.7. Prior Research and Established Digital Transformation Readiness Level Models of Building Facility Management in Construction

Numerous digital transformation initiatives exist, yet they remain divided into discrete areas, and there is currently no definitive method to assess the level of digitalization in building FM. Reference [1] elaborated that there exists a substantial body of literature concerning FM at present, but no research article systematically examines the contribution of the Fourth Industrial Revolution (Industry 4.0) while integrating DT into FM and linking it with the performance of FM organizations. Reference [2] underscored the importance of conducting a comprehensive analysis of present implementations of disruptive technology integration with FM by analyzing existing constraints and pinpointing possible directions for future research.

Reference [41] identified a readiness model whereby construction firms will be able to comprehend and assess their organizational preparedness for the implementation of digital technologies using a readiness model and a self-assessment tool developed for this purpose. Nevertheless, construction firms continue to face a shortage of tools and direction in terms of assessing their technological adoption performance and attaining technological readiness or competence [42].

Reference [41] mentioned that some studies have focused on the application of digital technologies but have not measured the factors affecting DT. According to the author, existing studies have investigated DT readiness assessment and used the technology–organization–environment (TOE) model to incorporate critical indicators and indicator components into a digital technology readiness model to illustrate the readiness capabilities of an organization. Reference [43] utilized the technology acceptance model (TAM), theory of planned behavior (TPB), and diffusions of innovation (DOI) theories to analyze the perspectives of European business sectors. Reference [12] proposed a big data readiness assessment tool specifically for the Singaporean construction industry, while Ref. [44] explored the impact of image, external control perception, and voluntariness on AR-BIM implementation. Reference [45] investigated 3D printing readiness in the Australian construction industry using the TOE framework. The TOE and unified theory of acceptance and use of technology (UTAUT) models were utilized, respectively, by [46,47] to examine BIM adoption factors in the construction industry. Reference [48] introduced a human–organization–technology fit model to offer guidance on the implementation of BIM within construction organizations in Hong Kong. Smart contracts and IoT integration in Austrian construction were evaluated using the TOE framework by [49]. As a result of their analysis, critical determinants were identified regarding the opportunities and challenges associated with the integration of smart contracts and IoT in the Austrian construction industry. Moreover, ref. [50] suggested a decision-making framework to ascertain the organizational readiness determinants for UAV adoption in the Indian construction industry. Lastly, ref. [51] introduced a multi-criteria decision-making model to evaluate the strategic readiness of firms to implement Construction 4.0 technologies, but did not include the technological aspect of this. Rather, they concentrated on organizational, relational, and human factors. According to the information available to the authors, this is the first time that SEM has been implemented for the purpose of measuring the DT readiness level index in building FM. The DT of FM is still in its early stages, and the existing literature does not provide a parametric tool that can measure how far the DT has come in the FM of a given building. Therefore, a tool to determine the DT readiness of FM activities within an organization is crucial and should be the starting point for determining how much of a given building, firm, or organization is digitalized and which areas need DT initiatives. As a result, an innovative assessment framework for digitalization measures was developed, which will subsequently enhance the performance of FM.

3. Digital Transformation Readiness SEM in Building Facility Management

This section describes the creation and implementation of a comprehensive, multidisciplinary evaluation system for determining the digital transformation readiness level index in building facility management (DTRLIBFM) using SEM. The DTRLIBFM is a systematic approach to FM that makes it possible to comprehend the level of digitalization preparedness needed to deliver high quality operations and services. Each construct has distinct digitalization readiness metrics linked to it in order to assess its efficacy. The set of twenty indicators includes technological use, policies that support DT initiatives, and the proper infrastructure as enabled by the organization’s FM strategy and management. This enables better data utilization for building system control and monitoring, asset and property management, and user-friendly services for the entire facility. The DTRLIBFM framework (indicators) comprises twenty important elements of success. These indicators are divided into three (construct) pillars, as shown in Figure 2. The three categories of infrastructure, policy, and technology are seen as the primary pillars supporting the implementation of DT in building FM.

Group 1 focuses on implementing technology in buildings, achieving distinct data flow outcomes, and ensuring the storage of all building asset information in a common data environment. This will enhance the management, extraction, and organization of all assets, facilitating improved asset and building facility utilization. Group 2 entails creating rules and regulations that support the DT of structures and amenities in order to guarantee appropriate management through personnel education, data sharing, and internal and external specifications. Group 3 is made up of the hardware and software necessary to ensure that data are transmitted through the proper channels. Together, these different structures promote information exchange and improve the accuracy of the decision-making process. For example, without proper infrastructure and clear laws, it is difficult to apply BIM technology effectively.

This section is a deep dive into the use of the SEM as an assessment tool. SEM provides a reliable statistical approach to the elaboration of events, facilitating the examination of structural connections as well as causal processes [52]. The capability of SEM to study relationships between factors and constructs has made it attractive for utilization in diverse fields, including construction management and building FM. For instance, ref. [53] used partial least squares structural equation modeling (PLS-SEM) to study covariances and complex cause-and-effect relationships in order to determine the quality of social infrastructure projects. Moreover, ref. [54] conducted research investigating the factors affecting the performance of the FM of buildings using SEM, providing important recommendations for the FM department, which is responsible for managing soft and hard facilities. Another study was conducted by [55] to identify the key success factors for contract performance in the construction sector. The study used SEM with fuzzy network analysis to determine the relationships that influence contract management. Reference [56] used SEM to investigate the relationships between various factors involved in IoT adoption and knowledge management. Reference [57] conducted a comprehensive assessment of the utilization of SEM in construction research as documented in prominent construction research journals between 1998 and 2012. The researchers noted a substantial and pervasive integration of SEM throughout this period. Additionally, ref. [58] leveraged a hybrid approach consisting of SEM and a fuzzy neural network to assess a framework aimed at improving safety management in the construction sector. In doing so, they illustrated the potential of SEM analysis to depict the interaction between variables within a logistic regression model. Moreover, ref. [59] asserted that SEM is unique in that it can evaluate and examine the interconnections between constructs, a capability that distinguishes it from alternative methodologies.

SEM is different from other linear models because it combines different measurements to show constructs and considers the errors that come with each measurement, thus increasing the accuracy of the results [59]. SEM also facilitates the comprehensive statistical testing of models through its confirmatory approach, thereby bridging the gap between theory and observation [52]. Specifically, within the domains of construction management and engineering, this article elucidates the distinctive benefits of SEM when applied to the examination of intricate associations and extensive datasets [57]. SEM, due to its comprehensive range of capabilities, serves as a powerful tool for enhancing our understanding of DT in FM and offering insights for strategic decision-making in this critical field. Therefore, this study utilized the structural equation modeling AMOS to test and analyze interrelationships among indicators and constructs of the DTRLIBFM model.

4. Research Methodology

During this sequential mixed approach study, a combination of qualitative and quantitative approaches was used for data collection and assessment. The following subsections provide a detailed description of each methodological stage.

4.1. Data Collection

As previously stated, the corpus of literature now available on DT in building FM is constrained in its capacity to recognize and tackle the crucial factors influencing its effectiveness in augmenting digitalization in general. In order to overcome this restriction, the authors reviewed a large number of important scholarly works to identify the essential elements of the DTRLIBFM. The indicator and factor selection process incorporated an extensive range of publications from across the globe, with no limitations in terms of geography or journal associations. Furthermore, by integrating a significant number of literate reviews, one advances toward a comprehensive compilation, which facilitates the formulation of conclusions that are exceptionally generalizable [59]. After the data were retrieved, the most well-established success variables for DTRLIBFM in the building FM domain were ascertained and categorized using a systematic qualitative content analysis approach. As per [60], the procedure for evaluating qualitative content involves grouping data into discrete categories. After conducting a thorough analysis, the authors were able to create an index with 20 success indicators that function as DTRLIBFM indicators. These indicators comprise the three process groups of technology, policy, and infrastructure. By examining research studies on particular keywords, such as “building facility management”, “digital transformation”, “technology applications”, “policy”, “infrastructure”, “construction 4.0”, “smart buildings”, and “digital technologies”, this study, in keeping with earlier research, confirmed the success factors in the building FM phase.

4.2. Validation of the Identified Variables

To validate the success factors that were discovered via exploratory analysis, and to assemble a set of criteria for evaluating the DTRLIBFM, four experienced FM professionals were interviewed with a specific emphasis on digitalization as shown in

Table 1. Job experience and education background of participants.

Number	Organization Type	Current Role	Education Level	Years of Experience
1	Contractor	Engineering manager	Bachelor’s degree	20
2	Consultant	Engineering services specialist	Master’s degree	15
3	Consultant	BIM and digitalization specialist	Ph.D.	15
4	Client	Digitalization specialist	Master’s degree	18

. The criteria used for interviewee selection required interviewees to possess a considerable degree of expertise in managing and organizing facilities and buildings. Additionally, these individuals possessed a minimum of fifteen years of experience in FM and were actively engaged in digitalization endeavors within their respective national and international organizations. The interviews were carried out according to a semi-structured format, with two primary objectives: firstly, to identify and include any additional critical success factors pertinent to the DT process in building FM, and secondly, to eliminate unnecessary information and enhance the terminology employed.

4.3. Expert-Based Survey Questionnaire

4.3.1. The Design Process

Once the list of indices and constructs had been validated, each construct and indicator on the DTRLIBFM were gathered through a survey of expert participants. The survey was developed using SurveyMonkey, a web-based application that facilitates digital surveys. Contractors, clients, consultants, subcontractors, and suppliers with extensive knowledge of building FM and digitalization were provided with the aforementioned information. Several channels were utilized to distribute this survey, comprising SurveyMonkey databases, email, and social media platforms. It was distributed from November 2023 until February 2024, when the number of respondents required for this survey had been fulfilled. The objective of this survey was to assess the readiness level index for the DT of a building to ascertain the degree of digitalization during the FM phase in the construction sector. The questionnaire consisted of four sections. The first part focused on background information about the respondents.

Parts 2–4 encompassed a selection of literature about the factors impacting the DT in building FM, including technologies, policies, and infrastructure. These factors were collected to measure the readiness level of the DT in the FM of buildings. Respondents were then asked to rate these factors. Data pertaining to each factor were gathered through the survey and subsequently evaluated to examine their correlation with the corresponding construct. The data were further analyzed to determine the correlation between each construct (technology, policy, and infrastructure) and the DT readiness level index construct. The higher the DT readiness level, the more it affects the entire performance of the building lifecycle at a later stage. The responses provided in this segment ranged from ‘extremely important’ to ‘not important at all’ on a five-point Likert scale.

Table 2. Scale for the degree of significance.

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

provides specifics on the measuring tool used to quantitatively evaluate the indicators and constructs.

4.3.2. The Distribution Mechanism

The survey questionnaire for this study was distributed via purposive sampling [61]. Purposive sampling, a non-probability sampling technique, is extremely efficient when investigating a particular cultural domain with knowledgeable experts. Reference [62] highlighted the adaptability of this approach, which allows for the utilization of both qualitative and quantitative research methodologies. By securing skilled and experienced participants, this approach guarantees the generation of accurate and superior insights and results [63]. Identifying the research problem, establishing selection criteria, locating suitable participants, collecting data, and noting any biases are all components of the procedure [63]. To mitigate potential biases, the participants in this study comprised FM professionals selected from diverse geographic regions, and the data was collected worldwide as indicated in Appendix B.

4.3.3. Calculation and Determination of Sample Size

It is critical to perform a comprehensive analysis to determine that the collected data accurately reflects a wide range of perspectives, thus laying a strong foundation for subsequent examination. More than 70% of the 84 studies reviewed by [57] concerning the application of SEM in the disciplines of construction and management utilized a sample size of less than 200. Furthermore, according to [64], there are situations in which a sample size below 100 may be considered suitable, such as when there are few statistically significant variables and favorable characteristics. However, they also recommended a sample size greater than 200 as the optimal approach. There is currently no agreement on the ideal sample size for SEMs according to [65]. The survey in this study was completed by a total of 220 participants. According to [66] and the previous discussion, the sampling size appears to be precise in order to guarantee that the parameter estimates are accurately and consistently generated during the estimation procedure. A total of 668 potential respondents were invited to participate in the survey; however, only 336 of them provided a response. Additionally, the presence of 89 incomplete responses and 27 outliers resulted in a decrease in the total number of responses from 336 to 220. This corresponds to an estimated response rate of around 33%.

5. Respondent Demographics

The type of profession, organization, and sector each survey respondent worked in, along with how many years of professional experience they had, was recorded. This clarifies the sample tested through the survey, making it easier to analyze the logic of the responses and similarities between them. Data on the respondents’ professions and the nature of their work obtained from the survey are illustrated in Figure 3a. The analysis highlights that a significant proportion of the participants (63%) occupied managerial positions within their respective companies, working in roles such as executive manager, department manager, facility manager, or project manager in the field of building facility management. It is also significant that 37% of the participants held technical positions, working as senior facility engineers, facility engineers, quantity surveyors, or other technical roles.

Examining the data on the type of organizations the participants worked for in Figure 3b, it is worth noting that 48% and 28% of the participants, respectively, were associated with owner/client and consultant firms which work in the field of building facility management and participate in digitalization initiatives. Additionally, 14% of the participants were contractors, 6% were suppliers, and 4% were subcontractors. Figure 3c illustrates the distribution of the sample among different economic sectors: 42% of respondents represented the private sector, 30% worked in the public sector, and only 28% worked in the semi-government sector. This indicates differentiation in the sample between different fields and sectors. The dataset is also representative of individuals who possess a high level of expertise in the respective field. Figure 3d shows the number of experienced professionals in the given sample. It segregates the respondents by years of experience: 40% of the participants were highly experienced, with more than 16 years of professional experience. The majority (about 46%) of participants had a medium level of experience (11 to 16 years). The remaining 14% had 10 or fewer years of experience.

6. The Structural Equation Model

This research applied a two-stage approach to establish a solid framework for examining the relationship between different constructs and indicators in DTRLIBFM. Confirmatory factor analysis (CFA) was used in combination with the SEM to assess the validity and reliability of the model. CFA was applied to the proposed DTRLIBFM model to ascertain the correlation between the primary constructs and the factors (indicators). Three fundamental latent constructs systematically categorized the measurement framework, which includes a total of 20 indicators. In the interim, by employing SEM, the secondary construct reflects the DTRLIBFM digitalization index. The bootstrapping maximum likelihood technique was utilized to estimate structural paths and factor loadings [52].

6.1. Model Specification, Estimation and Classification

By outlining the hypothesized relationships and their corresponding equations, the model specification establishes a solid conceptual model. Model estimation comprises the selection of an appropriate approach to determine the model parameters, while model classification guarantees the existence of an independent numerical solution for the designated model. As illustrated in Figure 4, the measurement framework consists of 20 indices classified into three latent constructs or groups: G1 represents the technology factor, G2 is for the policy factors, and G3 expresses the infrastructure factors.

The development of a structural model facilitates anticipation of the relationships between primary and secondary constructs. As illustrated in Figure 4, this structural framework consists of three primary latent constructs: technology, policy, and infrastructure. A reflective secondary construct known as the DTRLIBFM is interconnected with these constructs. In accordance with the DTRLIBFM framework, the three first-order constructs mentioned above exhibit a significant correlation with the DTRLIBFM, implying a positive association. The measurement model is developed through trial and error, which often enhances fit and guarantees accurate parameter estimations. This essential step reduces the mean discrepancy between observed and model-predicted data and minimizes variable estimate standard errors to create a strong and appropriate fitting measurement model using the chi-square statistic, RMSEA, and comparative fit index (CFI), which will be discussed in detail later. Two primary hypotheses were developed in accordance with these assumptions; the initial hypothesis was subsequently subdivided into sub-hypotheses. The following are the principal hypotheses that inform this analysis:

H01. 

The analysis reveals that each of the three primary constructs has a positive impact on the DTRLIBFM model.

H02. 

The integration of the three constructs enhances the overall level of DT in building FM.

6.2. Goodness of Fit Indices

Goodness of fit (GOF) measurements are of crucial importance when it comes to model improvement and determining the efficiency of item fits when assessing the underlying constructs. Although the literature has documented several tests for evaluating model fit [57], a consensus regarding the most suitable indices has yet to be reached. In accordance with the suggestions proposed by [66,67,68], a number of standardized metrics were employed to assess how well the model fit the goals of this study: the relative chi-square (χ²/df), root mean square error of approximation (RMSEA), and comparative fit index (CFI) were utilized. The degree of variation between the expected and observed covariance matrices was quantified using the chi-square (χ²) value [64], whereby a greater value of χ² signifies substantial inconsistencies in the covariances of the implied and sampled data. The ideal size was found using the relative chi-square (χ²/df), which should ideally fall between 1 and 3 [12]. Moreover, the CFI measure was used to evaluate agreement between the proposed model and the observed data. CFI values range from 0 to 1, with a minimum threshold of 0.90 considered satisfactory [66]. RMSEA measures covariance variation in comparison to a saturated model; authors of [66] generally accepted values below 0.08. In this study, indicators with a factor loading of 0.40 or less were generally excluded to preserve the integrity of the model [69]. As shown in Figure 5, the DTRLIBFM model was subsequently modified by excluding G01.13 and making the necessary adjustments to the indices.

The GOF indices support the viability of the modified measurement model, as shown in

Table 3. Goodness of fit indices for the initial measurement model.

Indices Measure	Representation	Estimate	Threshold Limit	Interpretation
Chi-square	χ2	367.041	-
Degree of freedom	df	167	-
Chi-square divided by degree of freedom	χ2/df	2.198	Between 1 and 3	Excellent
Comparative fit index	CFI	0.921	>0.90	Excellent
Root mean squared residual	RMR	0.022	<0.07	Excellent
Root mean square error of approximation (RMSEA)	RMSA	0. 074	<0.08	Excellent

. Additionally, the computed χ²/df value (2.198) is less than the 3.00 threshold that [70] recommends. Furthermore, the value of 0.921 for the CFI surpasses the acceptable threshold of 0.90. At 0.022 and 0.074, respectively, the root mean square residual (RMR) and root mean square error (RMSEA) of the approximation values are below 0.07 and 0.08, according to [66,71], who specified the cut-off values. All these results support the notion that the confirmatory factor model (CFM) satisfies the requirements for a well-fitting model. The absolute fit, which encompasses the key indices RMSEA and chi-square to enhance the model’s fit, falls within the acceptable range. CFI calculated the incremental fit aspect and, as previously stated, yielded acceptable values, indicating that the target model remains in excellent condition. Parsimonious normed fit index (PNFI) and parsimony comparative fit index (PCFI) tested the parsimonious fit indices with values of 0.70 and 0.78, respectively.

6.3. Reliability and Validity of the Measurement Model

It is crucial to evaluate the model’s reliability and accuracy after adopting the best estimation technique before presenting the structural model. According to [57], a critical first step in assessing a model’s reliability and reinforcing its theoretical foundations is to validate the construct. Finding uniformity and using Cronbach’s alpha to assess construct consistency are two aspects of reliability assessment. Evaluating both convergent and discriminant validity is necessary to determine the validity of the model. Employing the SPSS v29 tool, the Cronbach’s alpha (α) test was used to assess the reliability of the questionnaire indicators, with a coherence threshold of at least 0.7 [66].

Table 4. Cronbach’s alpha and composite reliability coefficients of the latent variables.

Construct	Cronbach	Composite Reliability (CR)
Technology	0.891	0.94
Policy	0.905	0.95
Infrastructure	0.908	0.95

provides a detailed breakdown of the evaluation’s findings.

Every construct had Cronbach’s alpha values higher than 0.7. Consequently, the respondents’ feedback was considered reliable and coherent enough for additional research, suggesting a cohesive construct considering the variety of indicators. The standardized factor loadings (SFLs) method was applied for unidimensional analysis. All indicators had positive SFLs that exceeded the threshold of 0.4 suggested by [69], as shown in Table 4. As a result, the one-dimensionality requirement was successfully met by the DTRLIBFM measurement model.

6.4. Testing Convergent Validity

Convergent validity (CV) is defined by [72] as the extent to which various estimates of a certain construct—which are assumed to be interrelated based on fictitious frameworks—show interconnectivity. If all a construct’s SFLs exceed 0.4, as mentioned by [69], and composite reliability (CR) is greater than 0.7, according to research by [66], then CV is satisfactory. In order to calculate CR, the total factor loadings for each construct (Li) must be squared, and the total error variance terms of these constructions must be taken into account (ei). Equation (1), as presented in the research of [66], can be utilized to provide additional insight into this procedure.

In Equation (1), SFL is represented by “Li”, each item in a set is denoted by “i”, the total number of items is represented by “n”, and the construct “i”’s error variance is represented by “ei”. According to [73], CR is the only method that researchers can use to ascertain satisfactory CV. Table 4 presents findings which indicate that every construct in the present study had a CR value greater than 0.70. Furthermore, as shown in Figure 6, the convergent validity of all the SFLs was found to be within the limits deemed acceptable by [57,74]. The findings indicate that the model is extremely consistent and that the construct possesses a high degree of internal consistency [73]; the values obtained for CR and SFL effectively indicate that the convergent validity requirements are satisfied.

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

6.5. DTRLIBFM Structural Model

SEM was used to develop a conceptual framework that shows the direct effects of each construct on the DTRLIBFM. The structural model shows how much each component in the model is influenced by the main construct, as shown in Figure 6.

An overview of the outcomes from fitting the aforementioned structural model is given in

Table 5. Goodness of fit indices for the modified measurement model.

Indices Measure	Representation	Estimate	Threshold Limit	Interpretation
Chi-square	χ2	351.514	-
Degree of freedom	df	149	-
Chi-square divided by degree of freedom	χ2/df	2.359	Between 1 and 3	Excellent
Comparative fit index	CFI	0.922	>0.90	Excellent
Root mean squared residual	RMR	0.023	<0.07	Excellent
Root mean square error of approximation (RMSEA)	RMSA	0. 079	<0.08	Excellent

. The results satisfy every goodness-of-fit (GOF) index, indicating that the structural model meets the criteria for a suitable fit.

The estimated variance (R2) was used to evaluate the relevance of the correlation between the DTRLIBFM model and second-order constructs once the model GOF was at an acceptable level. R2 should be more than 0.50, per [12,66]. The data in

Table 6. DTRLIBFM constructions’ standardized factor loading values and variance-explained percentages (R2).

Construct	Standard Factor	Estimated Variance (R2)
Technology	0.81	0.656
Policy	0.90	0.81
Infrastructure	0.82	0.672

show that, in line with [69], every single SFL value exceeded the 0.4 criterion. These findings point to a close connection between DTRLIBFM and the altered constructs.

As a result, the three first-order constructs demonstrate notable positive influences, offering solid proof in favor of the theory that these constructs can be used to assess the DTRLIBFM in the facility management of buildings.

7. Analysis of the Survey Data

By leveraging an SEM, this study efficiently calculates and ranks the significance of numerous comprehensive indicators and constructs linked to degrees of preparedness for DT in building facility management, relying on their respective SFLs. This section presents and discusses the results of this study, emphasizing that the constructs of the DTRLI have a significant and positive impact on the DT of FM. Additionally, a future study could investigate whether there is a positive correlation between DT and the key performance indicators of building facility management. This section summarizes the process of synthesizing and analyzing the accumulated data, providing significant insights into the results obtained.

7.1. Validation of Survey Data

The survey was digitally distributed to a wide variety of international industry experts to evaluate the impact of each construct and measure DTRLIBFM. Feedback was collected from a wide range of experts, including consultants, clients, facility managers, project managers, and departmental managers, as well as suppliers and subcontractors. The distribution channels for the expert-driven survey included email and multiple social media platforms, as detailed in the Research Methodology Section. Out of a total of 668 potential respondents, 336 responses were obtained. Out of the total responses received, 220 were deemed complete and valid; the remaining responses were omitted on the grounds of incompleteness or outlier status. The response rate of around 33% is near the mean response rate of 34% documented by [75] for online surveys. Based on the thorough examination that was performed, it is justifiable to assume that the data obtained from the survey are reliable and comprise an all-encompassing sample of stakeholders in the FM sector, thereby enhancing their credibility.

7.2. Constructs’ Ranking Comparisons Amongst the Respondents

The relative importance index (RII) was utilized by participating experts from diverse sectors and organizational structures to assess and rank the relative importance of each construct in accordance with the survey response data presented in

Table 7. Ranking of construct importance by all respondents.

Group Name	RII	Rank
Policy	0.824	1
Infrastructure	0.817	2
Technology	0.789	3

. In addition, comparisons were made of the rankings provided by each respondent group in order to assess diverse perspectives on the DT building facility management constructs. The calculation for the RII is as follows: RII represents the relative importance index; w corresponds to the weights assigned to each construct by respondents and ranges from 1 to 5; X represents the frequency of responses for each weight; A represents the highest weight (5); and N represents the total number of individuals who participated (220 for the entire survey). The RII is a numeric scale with a value between 0 to 1, with a higher value signifying that a construct is relatively important in comparison to others.

$$RII={\displaystyle \sum }_{i=0}^5{\displaystyle \frac{wi\ast Xi}{A\ast N}}$$

Ranking of Construct Importance by All Respondents

This study also captured the diverse perspectives of stakeholders, providing a more comprehensive understanding of their perceptions within the FM industry. This allowed for this study to further compare various groups and determine which areas of interest are subject to specific constraints based on prioritization. The purpose of this section is to evaluate and compare different organizations, recording the importance of each group based on its factors. Given the differences in business and operational models among the organizations (clients, consultants, contractors, subcontractors, and suppliers), the average rank group was estimated by averaging the sum of the factors’ ranks and comparing them for each group. Finally, the group that had the lowest ranking record was considered top of the list in terms of importance. The group with the lowest group average (GA) value was ranked first, as illustrated in

Table 8. Ranking score value calculation in accordance with the stakeholders involved in the survey.

	Client			Contractor			Consultant			Supplier			Subcontractor
Factors	Rank	AR	GR	Rank	AR	GR	Rank	AR	GR	Rank	AR	GR	Rank	AR	GR
G01.01	15	10.7	2	15	9.5	1	16	10.3	2	5	10.9	2	13	10.9	2
G01.02	2			5			13			6			14
G01.03	1			8			7			7			1
G01.04	6			11			14			8			18
G01.05	11			9			19			19			6
G01.06	12			7			1			2			20
G01.07	13			6			2			15			2
G01.08	7			3			17			9			15
G01.09	17			16			15			10			7
G01.10	9			4			4			11			3
G01.11	8			18			18			18			16
G01.12	19			20			6			20			19
G01.13	20			2			3			12			8
G02.01	5	5.5	1	17	11.7	2	9	9	1	16	15	3	17	11.7	3
G02.02	4			1			10			13			9
G02.03	3			19			12			14			10
G02.04	10			10			5			17			11
G03.01	14	16	3	12	13	3	11	13	3	3	2.6	1	12	7	1
G03.02	18			14			8			1			4
G03.03	16			13			20			4			5

Note: AR—average rank; GR—group rank.

. The table show the hierarchical importance of ranking groups based on five types of stakeholders (client, contractor, consultant, supplier, and subcontractor). Moreover, it presents various groups, further classified into technology, policy, and infrastructure domains.

The client and the consultant emphasized the importance of policy factor considerations, particularly given that the majority of clients are semi-governmental and governmental organizations. This makes the data more factual, since clients and consultants collaborate to manage strategies and regulations from a policymaking perspective. Additionally, most consultants are heavily involved in acquiring the necessary training for employees and in digitalization strategies; these are top priorities for clients aiming to enhance the adoption of digital innovations and human capital. Contractors prioritized technological considerations over infrastructure and policy groups, recognizing the crucial role these components play in enabling successful DT initiatives, as contractors are solely responsible for implementing cutting-edge technology. This strongly supports the argument that contractors spend money on research and development to satisfy the technical specifications and requirements of clients adopting DT in building facility management. On the other hand, subcontractors, and suppliers emphasized the development of resilient infrastructure, acknowledging its pivotal role in facilitating the integration of digital technologies into building facility management. All stakeholders acknowledged the crucial role of uninterrupted data transfer in DT. They all prioritized policy and infrastructure considerations, except for contractors, as they facilitate the effective execution of DT and outline essential tasks for each FM team within the organization. Additionally, infrastructure is used to continuously store records in digital archives for future reference, establishing a digital asset legacy for the organization.

8. Discussion of the SEM Results

This section presents an analysis of the SEM results, indicating which constructs are more significant and which factors are more important in relation to one another. When it comes to developing DT in the field of FM, construct-level analysis indicated that policy had the highest standardized factor loading score (SFL = 0.90). Strategic initiatives, regulations, technical specifications, and employee training are all vital elements of deploying DT in building facility management. As expected, the SFL of the factor G02-02 (policy and regulation) was highest (with SFL = 0.93), and strategy had the highest SFL (0.88). Because the increasing demand for speed and adaptability makes it strategically necessary for organizations to adopt state-of-the-art technologies, [76] highlighted the significance of technology as a strategic enabler rather than a mere tool. Therefore, proper policy, regulation, and compliance will govern the successful implementation of DT. Moreover, ref. [77] established that organizations which successfully integrate technology into FM prosper by achieving a technological advantage over their competitors. Technological adaptation is transformed from an optional undertaking to a strategic decision in the drive for operational excellence, cost reduction, and adaptability. This means that an organization should provide a clear strategy with a regulatory framework that can shape DT implementation in the FM phase.

Establishing regulatory compliance and strategic alignment ensures the achievement of organizational objectives in a methodical fashion [78]. After that, factors related to training and technical standards enter the picture, as shown by [6], who highlighted the critical importance of employee training and emphasized its pivotal function in augmenting technical competencies that match market necessities in the FM domain. The provision of technical specifications that match client requirements was the third most important SFL factor (SFL = 0.85). As stated by [26], facilitating employee growth via training programs not only provides opportunities to fully exploit technological advancements, but also provides personnel with competencies that are essential for effectively navigating them. Investing in technical training allows personnel to effectively adjust to developing technologies and maximize the effectiveness of implemented solutions. Furthermore, actively involving employees in the integration process creates a collaborative work environment in which their input guarantees that technological solutions are in perfect accordance with operational needs during the planning and implementation phases. Allocating resources towards providing training for personnel to facilitate DT increases knowledge and promotes the development of necessary competencies in the field of DT in building facility management.

The second most significant construct (SFL 0.92) highlights the importance of infrastructure, which includes centralized control mechanisms, data storage, and communication. The SFL score for factors related to data storage and control in the control center was high (0.91), indicating the crucial role of data storage in data centers or through cloud computing, and the equal importance of monitoring and controlling these data once integrated. Reference [34] recognized cloud computing as one of the most important forms of high-tech integral infrastructure for the management organizational data, especially when integrated with IoT and wireless networks. Smart building concepts rely on data storage methods and principles, making them accessible to all users in a more transparent environment. This has many advantages, such as data access and quick information sharing with no silo effect barriers between departments. Many organizations have therefore implemented cloud computing and smart buildings, which allow for effective management of asset condition and logistics. Systems facilitating communication, storage, and the IoT are required to realize the vision and implement a smart campus, supporting the principle of smart buildings. Moreover, the research pinpointed the application, network, and awareness layers that make up the IoT, facilitate the flow of data and provide control in the event of abnormal activities. Command control centers are useful for controlling building components. Such centers provide access to big data as well as the capability to generate continuous feedback for operators, thus facilitating intelligent and dynamic building control [79]. Therefore, data centers, cloud computing for data storage purposes, and the establishment of control centers to avoid any disruptive events are the core foundation of the principle of smart buildings and the DT aspect of FM.

Effective communication (SFL = 0.81) is a significant operating factor in FM, ensuring proper data storage and control. In addition, ref. [33] emphasized the implementation of 5G technology for diverse smart facilities management (SFM) applications, using the strategy of the Singaporean government as an example. The establishment of the 5G Advanced BIM Lab at the National University of Singapore, which aims to develop SFM use cases, is a component of this strategy. Effective communication, in conjunction with well-organized feedback systems, is critical for facilitating problem resolution and supporting consensus among the various parties involved in the facility. Effective communication is highly important, as it is a complementary process in which assets are connected continuously and, with no interruption, verify and validate the storing and copying of data.

The results emphasize the critical significance of technological progress in the field of FM: this was ranked as the third most crucial construct (SFL = 0.81) in improving building systems. This underscores the importance of incorporating contemporary data management technologies—including radio-frequency identification (RFID), computer-aided facility management (CAFM), building information modeling (BIM), and computerized maintenance management systems (CMMSs)—to streamline the management of assets and data. Moreover, organizations prioritize the integration of AI into decision-making procedures to ensure fast and accurate decision-making, as well as the establishment of resilient cybersecurity protocols to ensure data confidentiality and security, thereby minimizing data breaches.

The IoT, a significant technological advancement that is reshaping the operational domain of FM, functions through the transmission of data captured by sensors to a common data environment (CDE). This information can be used to generate digital replicas, also known as digital twins, and these can provide valuable insights that contribute to space optimization and digital asset management. In addition to improving operational efficiency, the integration of these technologies facilitates informed decision-making and proactive maintenance, thereby enhancing the overall efficacy of FM practices.

Exploration of geographical information systems (GIS) is a particularly noteworthy construct due to its exceptional SLF of 0.75. The utilization of GIS in FM involves the combination of aerial and map data in order to streamline safety surveillance, environmental monitoring, and planning [1]. Digital twin technology, which provides improved visualization of operational capabilities that are vital for FM activities [2], follows GIS in significance (SLF = 0.74). By harnessing BIM-based methodologies, digital twins facilitate decision-making processes through the provision of all-encompassing building information, such as historical failure and anomaly data and maintenance information.

AI, which is of the utmost importance in data analytics for supporting well-informed decision-making, is ranked third, with SFL 0.72. In their study, ref. [80] illustrated the ways in which IoT solutions improve user-built environment connectivity through the utilization of AI-powered intelligent digital interfaces. AI is also utilized in the healthcare sector as a predictive diagnostic instrument, with implementations including the generation of healthcare reports and the differentiation of normal and infected chest X-rays amid the COVID-19 pandemic [8].

Moving to the fourth most important (SFL = 0.71) factor, [20] identified a robotics framework for FM duties including painting, cleaning, and inspection, which has enormous potential for soft services in FM. Furthermore, the application of AR and VR in the field of facility management has increased in importance [3], exhibiting the fifth-greatest SFL at 0.70. This encompasses the use of BIM, digital twins, and AR and VR devices to oversee the state of assets and enhance the management of their lifecycles.

9. Conclusions

Traditional methods of handling data and assets are becoming less and less relevant, and companies, advisors, and suppliers must therefore rapidly adjust to the use of digital activities to maintain competitiveness. The absence of empirical research regarding the connection between the success of FM companies and their readiness for DT highlights the importance of conducting comprehensive investigations into the digitalization of assets, buildings, and facilities in order to guarantee future resilience.

To close this gap, the current study offers a thorough DT readiness tool that comprises three constructs and twenty indicators for assessing the degree of DT that has taken place in facilities. The purpose of this study is to establish the level of digitalization and the repercussions that it has for FM businesses by applying SEM to evaluate digitalization readiness criteria. The significance of these indicators was validated by industry professionals using data obtained from 220 FM practitioners from around the world and analyzed with SEM and SPSS AMOS (version 26). The DT of FM is heavily influenced by key performance indicators, especially when it comes to adopting new technology, creating appropriate policies, and ensuring that strong infrastructure is in place to facilitate seamless data transfer between different systems and the collection of data in a shared environment. These elements will form the basis for implementing DT in a particular facility. The model indices and standard loading factor verified the model analysis hypotheses’ accuracy and validity. A positive correlation exists between DTRLIBFM’s three main factors and latent variables. The second hypothesis, which asserts that integrating these three components enhances the digital transformation of building facility management, was validated. The findings demonstrate how important a role key performance indicators play in the DT of FM.

This study offers the concept of a quantifiable digital transformation readiness level index in building facility management (DTRLIBFM) as a tool for the planning, monitoring, and evaluation of digitalization competence and readiness. The model offers valuable insights that can guide the development of improvement and priority strategies. Executives are thus provided with the capacity to properly estimate the amount of DT that is being achieved using the SEM tool, with a high level of emphasis being placed on certain aspects of DT. The DTRLIBFM is a valuable tool for firms to implement, and through it they can move toward digitalization projects. In the realm of FM, the contributions of this study include the provision of a pragmatic framework for the execution of building digitalization evaluations, addressing present research deficiencies in the field of FM. Furthermore, the DTRLIBFM-SEM offers opportunities to benchmark and evaluate DT practices in building facility management. This is accomplished by laying the groundwork for subsequent research activities that will investigate alternative methodologies such as weighted synergy network (WSN), partial least squares structural equation modeling (PLS-SEM), and the analytical hierarchy process (AHP).