An-Najah National University 

 

Faculty of Graduate Studies 
 

 

 
 

 

 
 

 
 

DIGITAL TRANSFORMATION IN 

CONSTRUCTION MANAGEMENT: 

ASSESSING THE READINESS OF 

PALESTINIAN COMPANIES 

 

 

 

 

 

 
 

 
By 

Azmi Majed Azmi Qabaja 

 

 

 

 
 

 

 

Supervisor  

Dr. Muawia Ramadan 

 
 

This Thesis is Submitted in Partial Fulfillment of the Requirements for the Degree 

of Master of Engineering Management, Faculty of Graduate Studies, An-Najah 

National University, Nablus, Palestine. 

 
2025 



ii 

 
 

 

 

 

 

 

 
 

 
DIGITAL TRANSFORMATION IN 

CONSTRUCTION MANAGEMENT: 

ASSESSING THE READINESS OF 

PALESTINIAN COMPANIES 

 

 

 

 

 

 
By 

Azmi Majed Azmi Qabaja 

 
This Thesis was Defended Successfully on 17/06/2025 and approved by 

 

 

Dr. Muawia Ramad 

Supervisor 

 

Signature 

Dr. Mohammad Alnhal 

External Examiner 

 

Signature 

Dr. Mohammad Othman 

Internal Examiner 

 

Signature 



iii 

Dedication 

To the one whose name I carry with pride and honor, my first teacher, my father Majed 

Qabaja 

To the warm embrace perfumed with the fragrance of the homeland, my first support in 

life, my dear mother Hayat Qabaja 

To my dear wife, Kinana Dababsa, who stood by me and supported me with all love 

To my loving brothers who supported and encouraged me constantly 

Thank you for everything.  

  



iv 

Acknowledgements 

First and foremost, I thank and praise Allah, the Almighty, who gave me strength, 

patience and knowledge, and who was my support and helper during this academic 

journey, guiding my steps and facilitating the ways for me to complete this work 

successfully.  

There is no doubt that this achievement would not have been possible without the grace 

of Allah first, and then the efforts of everyone who stood by me and supported me 

during this journey. I extend my sincere gratitude and appreciation to my honorable 

supervisor, Dr. Moawia Ramadan, for his efforts and sound guidance, and for the 

support, care, patience and continuous motivation he provided to me, which had a great 

impact on the completion of this research. 

I would also like to express my deep gratitude to the faculty members of the 

Engineering Management Program at An-Najah National University, whose guidance 

and lessons were a fundamental pillar of this research. I thank the members of the 

discussion committee for their valuable time and efforts in reviewing and evaluating 

this work. 

Finally, I would like to thank all the participants in this study, who kindly shared their 

experiences and knowledge, and whose contributions played a pivotal role in enriching 

and completing this research.  

To all of you, my deepest thanks and gratitude. 

 

  



v 

Declaration 

I, the undersigned, declare that I submitted the thesis entitled: 

DIGITAL TRANSFORMATION IN 

CONSTRUCTION MANAGEMENT: 

ASSESSING THE READINESS OF 

PALESTINIAN COMPANIES 

 

 
 

I declare that the work provided in this thesis, unless otherwise referenced, is the 

researcher’s own work, and has not been submitted elsewhere for any other degree or 

qualification. 

 

 

 

  



vi 

List of Contents 

 

Dedication ........................................................................................................................ iii 

Acknowledgements .......................................................................................................... iv 

Declaration ........................................................................................................................ v 

List of Contents ................................................................................................................ vi 

List of Tables ................................................................................................................. viii 

List of Figures ................................................................................................................ viii 

List of Appendices ........................................................................................................... ix 

Abstract ............................................................................................................................ xi 

Chapter One:    Introduction ................................................................................................ 1 

1.1 General Background ................................................................................................... 1 

1.2 Problem Statement. ..................................................................................................... 4 

1.3 Significance of the Study ............................................................................................ 5 

1.4 Objectives ................................................................................................................... 6 

1.5 Research questions ...................................................................................................... 7 

1.6 Study Hypotheses ....................................................................................................... 7 

1.7 Thesis Structure .......................................................................................................... 9 

1.8 Literature Review ..................................................................................................... 10 

1.8.1 Digital Transformation and Artificial Intelligence ................................................ 10 

1.8.2 Digital Transformation in Construction Project .................................................... 10 

1.8.3 Digital Maturity ..................................................................................................... 11 

1.8.4 Applications of AI in Construction Project ........................................................... 12 

1.8.4.1 Predictive Analytics ............................................................................................ 12 

1.8.4.2 Risk Mitigation ................................................................................................... 15 

1.8.4.3 Cost Control during the Project .......................................................................... 17 

1.8.4.4 Construction Site Assessment ............................................................................. 18 

Chapter Two:    Research Methodology ............................................................................ 21 

2.1 Overview ................................................................................................................... 21 

2.2 Target population and sampling procedure .............................................................. 22 

2.3 Questionnaire design ................................................................................................. 24 



vii 

2.4 Qualitative Interviews ............................................................................................... 25 

Chapter Three:    Data analysis and results ........................................................................ 26 

3.1 Interviews analysis .................................................................................................... 26 

3.2 Questionnaires analysis ............................................................................................. 29 

3.2.1 Study population .................................................................................................... 29 

3.2.2 Assessment of constructs implementation ............................................................. 31 

3.2.3 Assessment of the measurement model ................................................................. 33 

3.2.4 Assessment of the structural model ....................................................................... 39 

3.2.4.1 The determination coefficient (R²) ..................................................................... 39 

3.2.4.2 The determination coefficient (R²) ..................................................................... 39 

3.2.4.3 Predictive Relevance of the Model ..................................................................... 40 

3.3 Testing of Hypotheses .............................................................................................. 41 

Chapter Four:    Discussion and conclusions ..................................................................... 43 

4.1 Chapter overview ...................................................................................................... 43 

4.2 Discussion ................................................................................................................. 43 

4.3 Conclusions ............................................................................................................... 48 

Chapter Five:    Strategy of Digitalization in Construction Sector Framework ................. 49 

5. 1 Chapter overview ..................................................................................................... 49 

5.2 Model development .................................................................................................. 49 

5.2.1 Stakeholders and their role in digital transformation ............................................. 50 

5.2.2 Components of the proposed model ...................................................................... 52 

5.2.3 Model Success Factors ........................................................................................... 53 

Chapter Six:    Managerial Implications and Future Studies ............................................. 55 

6.1 Theoretical implications ........................................................................................... 55 

6.2 Practical implications ................................................................................................ 55 

6.3 Limitations and future research work ....................................................................... 56 

List of Abbreviations ...................................................................................................... 58 

References ....................................................................................................................... 60 

Appendices ...................................................................................................................... 68 

CDEFGب ............................................................................................................................... ا 



viii 

List of Tables 

 

Table 1:    Active Registered Construction Companies ..................................................... 23 

Table 2:    Characteristics of the companies and role of the interviewees ......................... 25 

Table 3:    Demographic characteristics of the study sample ............................................ 30 

Table 4:    Converting verbal answers according to the five-point Likert scale into 
numerical answers .......................................................................................... 32 

Table 5:    Evaluating the means of response degrees ....................................................... 32 

Table 6:    Levels of ADT, DM, and MP ........................................................................... 33 

Table 7:    Results of reliability and validity analysis ........................................................ 35 

Table 8:    Discriminant validity check (square root of AVE is shown on the diagonal in 
bold) based on Fornell-Larcker criterion method .......................................... 37 

Table 9:    Discriminant validity check using HTMT ........................................................ 37 

Table 10:    Assessment of formative constructs. .............................................................. 38 

 

  



ix 

List of Figures 

 

Figure 1:    The research model and proposed hypotheses .................................................. 8 

Figure 2:    Big data innovation diffusion & the role of conflict in adoption .................... 13 

Figure 3:    Neural network structure ................................................................................. 14 

Figure 4:    EMLA Prediction Architecture. ...................................................................... 16 

Figure 5:    Random Forest and Genetic Algorithm (RF-GA) structure ............................ 19 

Figure 6:    Research methodology .................................................................................... 22 

Figure 7:    The results of PLS -algorithm for research model .......................................... 35 

Figure 8:    PLS Bootstrapping (T-values) for the research model .................................... 42 

Figure 9:    Model for Digitalization in the Construction Sector ....................................... 50 

Figure 10:    Proposed model for developing digital transformation ................................. 54 

  



x 

List of Appendices 

 

Appendix A:    Questionnaire ............................................................................................ 68 

Appendix B:    Questionnaire ............................................................................................. 74 

Appendix C:    Multivariate Analysis (MANOVA) ........................................................... 80 

Appendix D:    Model Fit ................................................................................................... 83 

Appendix E:    Results of R2, f 2 and Q² value .................................................................. 84 

Appendix F:    Hypothesis testing results .......................................................................... 85 

 

  

 

  



xi 

DIGITAL TRANSFORMATION IN CONSTRUCTION 

MANAGEMENT: ASSESSING THE READINESS OF 

PALESTINIAN COMPANIES 

By 

Azmi Majed Azmi Qabaja 

Supervisor  

Dr. Muawia Ramadan 

Abstract 

This study investigates the readiness of Palestinian construction companies to adopt 

digital transformation (DT) using artificial intelligence (AI) tools, while identifying 

associated challenges and proposing strategic solutions. In light of low productivity and 

marginal profit margins in the construction sector, DT is seen as a driver for improving 

project performance and innovation. The study evaluates how strategy, technological 

infrastructure, and human resources affect DT adoption and examines digital maturity as 

a mediating factor. A mixed-method approach was adopted. Qualitative data were 

collected through 13 semi-structured interviews, while quantitative data were obtained 

via a structured questionnaire distributed to 333 companies, with 143 valid responses 

(response rate: 42.9%). The Partial Least Squares Structural Equation Modeling (PLS-

SEM) technique was used for data analysis. The findings reveal that the level of digital 

transformation adoption (ADT) was high (mean = 3.73/5), digital maturity (DM) scored 

a mean of 3.64, while project performance (MP) showed a very high level (mean = 

4.18). Among ADT dimensions, technology had the highest influence (mean = 3.96), 

followed by strategy and human resources (mean = 3.62 each). The structural model 

showed strong, significant relationships between DT adoption and project performance 

(R² = 0.712), with digital maturity partially mediating this relationship (VAF = 52%). 

Key challenges identified include weak digital skills, limited infrastructure, and 

regulatory constraints. In response, the study proposes a strategic framework that 

includes: (1) investment in modern digital infrastructure, (2) development of digital 

competencies, and (3) regulatory reform. These strategies aim to enhance digital 

adoption and ensure sustainable performance gains in the Palestinian construction 

sector. This research provides empirical evidence of DT readiness and contributes 

actionable solutions and policy recommendations to accelerate digitalization in 

developing construction markets. 



xii 

Keywords: Digital Transformation, Artificial Intelligence, Construction Management, 

Digital Maturity, Palestinian Construction Companies, Project Performance, Strategic 

Framework, Technological Infrastructure, Human Resources, BIM, IoT. 



1 

Chapter One 

Introduction 

This chapter provides a general overview of this research. It includes a general 

background, research problem, research questions, aims and objectives of the research, 

significance of research and finally thesis structure.  

1.1 General Background 

Currently, the world is facing continuous challenges that have affected the work of the 

construction sector. To solve these challenges, engineering has thought about 

technology as it constantly evolves. The modern link between engineering and 

technology aims to improve the standard of living. Relying on advanced technical 

programs and artificial intelligence has become an urgent necessity in various fields, 

especially in the construction sector, as these tools play a major role in improving 

project management  (Patil, 2019). 

Kim et al. (2023), pointed out that using digital technology in the construction sector 

enhances the ability to manage projects effectively, through precise control of costs and 

schedules, which leads to more accurate and efficient results (Kim et al., 2023). The 

process involves using digital technology to manage all stages of the project from 

design to implementation. Digital tools such as project management software provide 

the ability to accurately track project progress and monitor budgets in real time, which 

facilitating decision-making and course correction in case of any deviation (Kim et al., 

2023). Digitalization facilitates communication between all stakeholders such as 

engineers, contractors, and supervisors. This helps in effective coordination and reduces 

delays resulting from misunderstandings. The construction projects sector is an industry 

with significant impact at the local and international levels, but the sector has faced 

challenges of poor productivity and small profit margins (Pistorius, 2017). 

Authoritative national and international publications highlight the construction industry 

(CI), construction projects (CP), construction management (CM), and construction 

companies (CB) (Patil, 2019). 

The move towards digital platforms enhances the competitive environment, as digital 

technologies change how these projects are managed, and bring new benefits that 



2 

contribute to increasing efficiency and innovation, as this affects the areas of enterprise 

management, knowledge utilization, construction site optimization, and collaboration 

between projects. With the advances in the use of artificial intelligence in construction 

projects, several scientific questions arise: How can engineers adopt AI technologies in 

the construction field? And evaluating the pros and cons of applying artificial 

intelligence in construction operations  (Patil, 2019). 

Digital transformation is seen as developing an IT strategy related to all operations in 

the company, so digital transformation and the use and development of artificial 

intelligence applications have become a necessity for most companies to keep pace with 

technological developments in the world, but in the field of architecture and 

construction, digital transformation is relatively recent. Most traditional companies in 

this sector have not fully adopted digital transformation strategies (Mithas et al., 2011). 

This sector has historically relied on traditional methods of design and construction, 

which made it less responsive to technological developments compared to other sectors 

(Dolla et al., 2023). However, digital transformation has great potential to change 

business models in this field. Digital transformation allows design teams to operate 

concurrently across platforms like Building Information Modeling (BIM), facilitating 

real-time collaboration among designers, engineers, and contractors. This actually helps 

achieve earlier and more accurate design alternations which enhanced project 

management and modify risks. Such improvements resemble in the use of drones and 

laser scanning as tools that can accurately provide accurate positioning data. Yet, the 

ongoing use of digital transformation without having a well-established model of digital 

transformation can mostly cause uncertainty in the entire process (Mohammadi et al., 

2023).  

So, companies are expected to adopt certain strategies of digitalization whereby they 

need to have clear cut key roles, consider the impacts of such transformation on both 

social and environmental levels and finally state their technological and business 

objectives (Machado et al., 2021). The thought of emerging digital tools like the 

Internet of Things (IoT) and Artificial Intelligence (AI) into the real world of work has 

led to extreme methods of transformation (Zhou et al., 2018). Digital transformation has 

significantly help companies to modify and evolve business environment towards facing 

today's markets complexity and uncertainty. Here, digital transformation importantly 



3 

redefines typical models of business and reintroduce the concept of value through 

process and efficiency improvement beside that of customers. In consequent, the 

organizations with efficient competitive capacity enhancing their sustainability against 

constant market challenge (Hess et al., 2016; Veldhoven & Vanthienen, 2022; Verhoef 

et al., 2021). 

With the growing awareness among practitioners, researchers and policymakers of the 

importance of digital transformation, it has become clear that these initiatives are not 

limited to enhancing the internal performance of companies, but rather constitute 

strategic tools that can bring about fundamental changes in the entire industry  

(Sebastian et al., 2017), Good management, from the researcher’s point of view  

(Machado et al., 2021), depends on integrating data flows with stakeholders in order to 

expand the knowledge base and enhance communication according to the goals of 

digital transformation, and gain a comprehensive understanding of the appropriateness 

of the technological infrastructure, as well as the maturity level of digital technologies 

and artificial intelligence applications. These technologies are integral to digital 

transformation, with current examples including Building Information Modeling (BIM) 

programs and the utilization of drones for workflow monitoring (Chwiłkowska-Kubala 

et al., 2023).  

Technology is a key element in accelerating the digital transformation in construction 

projects, contributing to improved efficiency, reduced costs, enhanced safety standards, 

and increased productivity levels. Digitally transforming data – images and texts into a 

likely binary code helps businesses improve their operational processes with great 

advantages on the customers' part (Berlak et al., 2021; Verhoef et al., 2021). This has 

urges both forms and governments worldwide to digital tools in the construction sector 

as significantly maintain their ability of market competitiveness and sustainability on 

long terms (Armstrong et al., 2019). In reality, human resources play a significant role 

in making the process of digital transformation truly efficient and more sustainable. In 

that, they contribute to develop innovative skills and facilitate the practical part of the 

change towards digitalization. This requires reviewing of the companies' HR system 

towards achieving a well-technologically developed system (Smirnova et al., 2019). 

  



4 

1.2 Problem Statement 

The industrial sector, including construction, tends to apply digital transformation. 

Construction companies has even gone beyond through emerging digital tools and 

artificial intelligence apps. This has actually signified the significance of digitalization, 

which helps simplify construction project management  (Naji et al., 2024). 

Nevertheless, the complexity of construction projects usually impedes the 

standardization of digital solutions. This reflects the lack of unified strategies, 

inconsistent standards and regulations and a shortage of experienced leaders in digital 

transformation management (Machado et al., 2021). Moreover, the current staff lacks 

the required digitalization-related skills. To fill the gap, construction companies either 

need to provide the employees with intensive digitalization training programs or employ 

well-qualified people in the field. Here, companies are recommended to provide their 

employees with the required digital knowledge and experience to fully manage the 

emergence of such digital transformation in practice (Chwiłkowska-Kubala et al., 

2023). More importantly, digital transformation maturity represents a significant factor 

as a medium between digital transformation in practice and those expectations to finally 

achieve. In fact, the higher the level of digital maturity, the better the status that a 

company is eligible to gain in terms of effectively adopting and integrating new tools of 

digitalization. 

Digital Transformation has fundamentally become part of modernizing the construction 

sector companies so as to achieve efficiency and sustainability. To effectively 

implement digital solutions, construction companies need to state well-definite 

strategies, have eligible leadership, reinvest in their staff training, and afford more for 

development.  

Since the Palestinian process of digitalization witness slowness, it requires a hard work 

to evaluate the current situation. The present study aims to evaluate the construction 

company's readiness and tendency to emerge digital transformation and the potential 

influence on their performance in a consequence. 

  



5 

1.3 Significance of the Study 

The present study gains its significance as it addresses the major challenges that 

Palestinian construction companies face upon adopting digital transformation tools and 

emerging them into practice. Through assessing such companies' readiness to emerge in 

digitalization, the study looks into the gaps and opportunities for enhancing efficiency, 

innovation, and sustainability. Here, the study intends to make a theoretical framework 

of modernizing traditional practices, improving project management, and boosting 

competitiveness. So, the study is expected to provide valuable vision in response to the 

technological improvements and foster long-term growth in construction companies, 

Specifically, the study seeks to: 

1. Bridging a knowledge gap in the Palestinian context, as there are no systematic 

quantitative or qualitative studies evaluating digital transformation in local 

contracting companies despite the accelerating global trend in this direction, 

especially in light of the increasing complexity of projects and the lack of digital 

competencies. 

2. Providing an integrated scientific model based on statistical analysis using the PLS-

SEM approach, linking three components (strategy, technology, and human 

resources) and the level of digital maturity, leading to their impact on project 

performance. This model provides an analytical tool that can be used later by 

researchers or other institutions. 

3. Providing accurate quantitative data to decision-makers in the government, the 

Ministry of Public Works, and the Contractors Union regarding: 

• The availability of digital infrastructure within companies. 

• The extent of the digital skills gap. 

• The degree of companies' responsiveness to technological changes. 

4. Proposing an executable digital strategy, based on real field results, that represents a 

roadmap for developing national policies in the field of digital transformation in the 

construction sector, in terms of: 

• Developing digital infrastructure. 

• Allocating financial or training support for contractors. 

• Formulating incentive guidelines linked to tenders or professional classification. 



6 

5. Enhancing the competitiveness of the Palestinian construction sector in a challenging 

economic and political environment by demonstrating how digital transformation can 

improve project quality, reduce costs, and increase adherence to schedules. 

6. Linking digital transformation to strategic planning for national projects, enabling 

official bodies to make decisions based on digital indicators and real readiness 

models, rather than general estimates.  

In brief, the present study gains its significance from the potentials to positively change 

the current practices of management in the field of construction projects, these changes 

really address today's challenges, enhance outcomes and contribute the progress 

generally achieved in instruction projects in the age of AI. 

1.4 Objectives 

The “Digital Transformation and Artificial Intelligence in Construction Projects” study 

is designed to guide the research process and achieve specific outcomes. The objectives 

are outlined as follows: 

1. Analyzing the readiness of Palestinian construction companies to adopt digital 

transformation across three main axes: 

• Available technological infrastructure. 

• Adopted management strategies. 

• Digital human resource competency. 

2. Assessing the level of digital transformation maturity among Palestinian companies, 

as an intervening variable linking companies' readiness to actual transformation in 

performance. 

3. Measuring the direct and indirect relationship between digital transformation 

adoption and construction project performance in terms of: 

• Schedule (time). 

• Cost. 

• Quality. 

• Scope of work. 

4. Developing an applied research model using the structural equation modeling (PLS-

SEM) method to measure the impact of digital transformation on performance based 

on the level of digital maturity. 



7 

5. Drafting an initial national strategy for digital transformation in the Palestinian 

construction sector, which can be used as a reference for decision-makers and 

stakeholders. 

Identifying the organizational, human, and technical challenges facing the digital 

transformation process, and proposing practical recommendations to address them at the 

institutional and political levels. 

1.5 Research questions  

This study seeks to address the following key questions: 

• RQ1: what extent are Palestinian construction companies ready to adopt digital 

transformation in terms of strategy, technological infrastructure, and human 

resources? 

• RQ2: What is the level of digital transformation maturity in Palestinian construction 

companies, and what are the factors influencing this maturity? 

• RQ3: How does adopting digital transformation impact construction project 

performance in terms of time, cost, quality, and scope of work? 

• RQ4: What are the challenges facing Palestinian construction companies in 

achieving digital transformation, and what are the possible solutions from an 

administrative and technical perspective? 

• RQ5: How can decision-makers use the results of this study to develop effective 

policies that support digital transformation in the contracting sector? 

1.6 Study Hypotheses 

The study aims to study the readiness of Palestinian Construction Companies to adopt 

Digital Transformation through assessing the different aspects such as strategy, 

technology and human resources and their impact on adopting digital transformation by 

enabling digitalization maturity measuring criteria such as (technological infrastructure, 

workforce skills, organizational culture, financial capability, and regulatory 

environment), then studying the impact of digital transformation on measuring the 

performance of construction projects. 

The figure below shows the conceptual model of this study which illustrates the impact 

of adopting digital transformation on the performance of construction project 

management by using digitalization maturity as a mediator in this relationship. 



8 

Figure 1 

The research model and proposed hypotheses 

 

Using the conceptual model provided, we can formulate hypotheses to explore the 

relationships between the adoption of digital transformation, the role of digitalization 

maturity as a mediating factor, and the performance of construction project 

management. The following hypotheses are proposed: 

H1: (Strategy, Technology, and HR).  

The Adoption of Digital Transformation has a Perceived Impact on construction project 

management performance:  

• H1a: strategy positively influences the readiness of adoption of digital transformation 

initiatives. 

• H1b: Advanced technology infrastructure positively influences the readiness of 

adoption of digital transformation initiatives. 

• H1c: Skilled human resources (HR) positively influence the readiness adoption of 

digital transformation initiatives 

H2: Adoption of Digital Transformation and Digitalization Maturity. 

H2: There is a significant positive relationship between the adoption of digital 

transformation and reaching digitalization maturity. 

  



9 

H3: Enabling digitalization maturity. 

H3: Enabling digitalization maturity significantly mediates the relationship between the 

adoption of digital transformation and construction project management performance. 

H4: There is a significant positive relationship between digitalization maturity and 

construction project management performance. 

H4: There is a significant positive relationship between enabling digitalization maturity 

and construction project management performance. 

These hypotheses aim to test the direct and indirect effects of digital transformation 

adoption on project performance, considering the mediating role of digitalization 

maturity.  

1.7 Thesis Structure  

The thesis is structured into Six chapters. The first chapter, Introduction, provides an 

overview of the research background, outlines the research problem, discusses relevant 

theories, and highlights the significance of the study. It also defines the research 

objectives, questions and Literature Review, examines existing research on artificial 

intelligence and its role in improving project productivity. It also explores topics such as 

automated planning, risk mitigation, cost control, and construction site evaluation, all 

within the context of advancing digital transformation. 

The Second chapter, Methodology, details the research approach, including the data 

collection process, target population, sampling methods, and the development of data 

collection tools. It also explains the analytical methods used. In the Thread chapter, 

Data Analysis and Results, both quantitative and qualitative data are analyzed, and the 

findings from hypothesis testing are presented. 

The Fourth chapter, Discussion and Conclusion, interprets the results from the thread 

chapter, draws conclusions about the validity of the hypotheses, and discusses their 

implications. The Fifth chapter, titled Digital Transformation Development Plan in 

Construction Companies, outlines a comprehensive strategy to support digital 

transformation within the sector. Finally, the Sixth chapter, Managerial Implications and 

Future Studies, highlights the practical and theoretical contributions of the research, 

addresses the challenges encountered, and offers recommendations for future studies. 



10 

1.8 Literature Review 

 This chapter will review and analyze both empirical and theoretical data from existing 

literature to highlight the significance of digital transformation and the application of 

artificial intelligence technologies, as well as their influence on construction projects. 

1.8.1 Digital Transformation and Artificial Intelligence 

Artificial intelligence is used as an effective tool in accelerating digital transformation 

processes, as it can analyze data and make smart decisions that enhance the performance 

of technological systems, making it one of the main drivers for developing modern 

technical solutions. It contributes to improving processes, analyzing data, and making 

decisions effectively. From an engineering perspective, artificial intelligence is used in 

analyzing and developing algorithms. Information is gaining much more value in a 

world with digital economy and effectively contributes well in companies' financial 

success in consequence. This actually enables companies to have well organized data, 

respond to variable changes and predict the future potentials. (Minbaleev, 2022). 

Additionally, Zaychenko et al. (2018) believe that digital transformation supported by 

AI tools can vitally help companies to reach their objectives and as their 

competitiveness. The integration of AI to digital transformation has positively affected 

various fields including; business management, construction companies, digital 

economy, human resources, healthcare, trade, community innovation, tourism and 

education (Zaychenko et al., 2018). consequently, decision makers can improve 

effective strategies, opting for the best ways of transforming into digitalization (Simoes 

et al., 2022). 

1.8.2 Digital Transformation in Construction Project 

The construction sector often encounters project delays, low levels of productivity and 

diction-making reluctance due to inefficient and inadequate adoption of digital 

transformation with slow turning into AI. fortunately, this has witnessed changes in the 

recent years urging more eligibility for digital transformation on the echo of labor 

shortages and the recent effects of COVID-19 pandemic (Sacks et al., 2020). In fact, by 

emerging innovative tools in the mid-1980s., the construction and design companies' 

adoption of AI enhanced their development (Abioye et al., 2021). These improvements 

have locally and regionally and globally made management integration with the 



11 

construction projects such significant, in a way that improved productivity with 

elevation of the construction sector scales, i.e., the US construction sector showed a 

share of nearly $900 billion during the first quarter of 2020 While maiming high-quality 

standards, the construction sector has also emerged innovative technologies that reduce 

carbon emissions in cement production. Moreover, aiming to achieve more accurate and 

efficient building design, the design sector witnessed the emerge of simulation and 3D 

modeling supported by AI (Weber-Lewerenz, 2021).  

1.8.3 Digital Maturity 

Digital maturity refers to the extent to which organizations develop and integrate digital 

technologies, strategies, culture, and processes to effectively compete in a digitally 

driven environment. This concept represents a gradual evolution from initial awareness 

of technology to a stage where digital innovation is a key driver of achieving a lasting 

and sustainable competitive advantage (Aras & Büyüközkan, 2023). The concept of 

digital maturity has gained increasing attention as organizations around the world 

increasingly recognize the importance of digital transformation as a critical factor for 

survival and growth. 

In the same context, several models have been developed to assess the level of digital 

maturity of organizations and guide them through their digital transformation journey, 

which often encompasses multiple dimensions such as technology, strategy, culture, 

governance, and organizational capabilities. For example, Aras and Boykozkan (2023) 

presented a comprehensive digital maturity model that integrates these dimensions into 

an integrated hierarchical framework. Their systematic review indicated that effective 

digital transformation requires a comprehensive assessment of maturity levels and the 

development of appropriate roadmaps for each stage (Aras & Büyüközkan, 2023). 

Similarly, Gokalp and Martinez (2021) proposed a Digital Transformation Capability 

Maturity Model (DX-CMM) specifically for industrial manufacturers, focusing on the 

balance between technical and organizational aspects. The model relies on standardized 

assessment methods such as SPICE levels to systematically guide improvements 

(Gökalp & Martinez, 2021). 

In a simpler yet practical framework, Hagg and Sandhu (2017) proposed a three-level 

digital maturity model comprising awareness, expertise, and autonomy, emphasizing the 



12 

role of leadership and change management as key enablers for facilitating progress 

between these levels, particularly in large organizations (Hägg & Sandhu, 2017). 

Additionally, Schumacher (2016) designed a multidimensional model with nine 

dimensions to assess the readiness of manufacturing organizations for the 

transformation toward Industry 4.0. The model encompasses both technical and 

organizational aspects such as strategy, leadership, and culture. The study demonstrated 

the model's applicability in smart factory environments (Schumacher et al., 2016). 

The concept of digital maturity has also been adapted to suit specific sectors, reflecting 

their specificities and challenges. For example, Wijnen (2020) presented a gradual 

digital maturity model for sports organizations, starting from analog and progressing to 

innovation, highlighting the importance of leadership and tailored strategies to foster 

digital adoption in this sector (Wijnen, 2020). Similarly, Kitkumpanat and colleagues 

(2023) developed an eight-dimensional digital maturity framework for sports media in 

Thailand, addressing culture, technology, organizational structure, customer 

engagement, strategy, operations, innovation, and data analytics, providing a nuanced 

and fundamental understanding of the level of digital readiness in this field 

(Kittkumpanat et al., 2023). 

1.8.4 Applications of AI in Construction Project 

1.8.4.1 Predictive Analytics 

Using a prototype to process- storing and analyzing- a huge database to fit a wide range 

of construction projects, Bilal et al. (2019) has developed a data-analytic approach to 

assess the profitability distribution in various construction projects. Here, data analysis 

showed that profit margins varied from time to time whereby that profitability 

performance is affected by various project parameters. To achieve a closer prediction 

accuracy, these findings are expected to integrate into a machine-learning algorithm. 

This approach has also modernized data analysis, uncovered the hidden patterns with 

regard to probability in various construction projects (Bilal et al., 2019). Being a 

valuable approach, the present study has explored the way the Agile methodology could 

be implemented in diverse environments. The research has therefore interviewed 

expertise project and big data managers. The interviews indicate the necessity to start 

with small scale implements, embrace minor setbacks as potentials for further constant 

improvements and adopt an interactive approach towards project development. Yet, 



13 

Challenges might appear when companies avoid or refuse applying cloud platforms or 

respond to solutions (Franková et al., 2016). With due regard to conflict management, 

Oyedele et al. (2020) examine the appropriate strategies of managing disagreements 

among teams in big database project companies. The present study, by contrast, insists 

the factors that could foster a culture of in-team-conflict-prevention. To achieve a 

conflict-free work environment, the researcher introduces four major factors; (i) 

effective-employees communication, (ii) team-competence in project management, (iii) 

proactive conflict management and (iv) effective project documentation. The study also 

introduces a framework of concepts aiming to improve human resource management 

through enhancing a conflict-proof environment of work so as to enable companies with 

big database opt for innovative as explained in figure (2) below (Oyedele et al., 2020). 

Figure 2 

Big data innovation diffusion & the role of conflict in adoption 

 
Source: Oyedele et al. (2020).  

Likely, Azami et al (2022) has developed a framework of data analysis using crane 

configurations in huge industrial construction projects. The framework integrates 

heuristic search techniques with artificial neural network (ANN) optimization 

algorithms, as shown in Figure 3 below. While empirical studies help professionals 

identify appropriate crane configurations based on specific technical requirements, the 

ANN model leverages historical project data to refine the process of choice. To validate 

the model's accuracy and reliability, a K-fold cross-validation method was employed, 



14 

demonstrating that the model achieved an accuracy rate of up to 70% on the validation 

dataset (Azami et al., 2022). 

Figure 3 

Neural network structure 

 

Source: Azami et al. (2022) 

Aiming at identifying how predictive analytics can improve the accuracy of cost 

estimation during the initial phases of a project. Castro Miranda et al. (2022) have 

investigated the role of statistical analysis in early cost estimation in construction 

projects. Castro Miranda et al. (2022) finally primarily focused on the significance of (i) 

creating a large database, (ii) highlighting the cost-affecting factors and (iii) comparing 

etween various methods of estimation. As a result, statics indicates the high level that of 

accuracy that the analysis methodologies have gained, making them such eligible in 

practice. Castro Miranda et al. (2022) also explained three main aspects as; (i) cost 

prediction analyses were not perfectly consistent with methodologies' practices and 

standards, (ii) the productivity of analysis techniques is eligible to work well in the 

construction industry and (iii) such analytic methodology makes a model for executive 

administers looking for adopting predictive analytics in cost estimation in deed, the 

enhancement of such tools is expected to enable stakeholders to have significant rples 



15 

toward improving cost prediction accuracy, which contributes well to produce an 

efficient budget management (Castro Miranda et al., 2022). Furthermore, Bilal et al. 

(2019) presented a convex-mathematical-optimization based approach to coordinate 

spatial designs and identify basic plan alternatives. This approach has two mathematical 

models; (1) the convex relaxation model, w relying on acyclic graphs to build a basic 

set of relative constraints that define spatial relationships within the design, which helps 

in categorizing the structure of spaces, (2) the optimizing geometry space model in line 

with the size of the modules used in the design. This methodology relies on aspect ratio 

constraints to limit the creation of multiple layouts with large variations. To help users 

achieve the same functional goals, the algorithm cabanas produce an alternative basic 

plan model, more organized and integrated in structure. More importantly, some of 

these layouts promote more efficient waste management, contributing to a reduction of 

waste by 8.75% through reducing material shredding and improving space utilization 

(Bilal et al., 2019). 

1.8.4.2 Risk Mitigation 

In response to the troublesome construction projects delays that the construction sector 

faces, Egwim et al. (2021) have developed an advanced model with a set of ensemble 

machine learning algorithm (EMLA) - see figure (4) Below. Based on Bagging, 

Boosting, and Naïve Bayes techniques, the model selected the quantitative technique to 

improve a predictive model through hyper-parameter tuning (Egwim et al., 2021). This 

model actually uses various techniques for analysis including; Decision Tree, Random 

Forest and Bagging. Other techniques include performance boosting algorithms such as 

Adaptive Boosting Classification and Regression Tree (CART), Gradient Boosting 

Machines, and Extreme Gradient Boosting. In consequent, a model with really 

complicated and multi-layer and stacking-based predictive model has been achieved to 

enhance the performance and precision of the EMLA approach in predicting delays 

(Egwim et al., 2021).  

  



16 

Figure 4 

EMLA Prediction Architecture.  

 
Source: Egwim et al. (2021) 

Examining the use of AI tools in the construction industry, K. Wang et al. (2023) has 

applied a fuzzy scenario environment using a framework of multi-criteria decision 

making (ACDM). This framework resembles aspects from Delphi method, the Analytic 

Network Process (ANP), and the Technique for Order Preference by Similarity to Ideal 

Solution (TOPSIS) (Wang et al., 2023). Systematically, the ANP model aims to assess 

the significance of AI tools depending on the expatiates input e fuzzy TOPSIS 

categorizes the most appropriate AI solutions for the construction sector. It seems that 

the technological factors are most of significance while those of environment come 

next, though the two factors critically make the AI landscape in the world of 

construction (Wang et al., 2023). However, Okpala et al. (2020) have investigated the 

real implementations of technological tools and devices for risk reduction within the 

sector of construction with due regard to efficiently adopting, perceiving and the cost 

implication of the needed technologies. Practically, analysis of the use of those tools 

and devices offers valuable insights to help managers in the field of construction be well 

informed upon making decisions concerning safety performance (Okpala et al., 2020). 

  



17 

1.8.4.3 Cost Control during the Project 

Elmousalami (2021) has reviewed 20 artificial AI techniques already applied in 

conceptual cost modeling. The techniques include fuzzy logic, mathematical model, 

multiple regression, case-based reasoning, as well as hybrid models like the genetic 

fuzzy model. Others include the ensemble methods like XGBoost and that of the 

random forest. Towards assessing effectiveness and accuracy of machine learning 

logarithm in the prediction of project cost, these models have been tested using the field 

channel improvement projects (FCIPs) (Elmousalami, 2021). Consequently. basing on 

mean absolute percentage error (MAPE) findings showed that XGBoost with AMAPE 

of 9.091% and an adjusted R² of 0.929 appeared to be the most accurate-reliable method 

(Elmousalami, 2021). The evaluation, based on mean absolute percentage error (MAPE) 

and adjusted R², revealed that XGBoost was the most accurate and reliable method, 

achieving a MAPE of 9.091% and an adjusted R² of 0.929 (Elmousalami, 2021). 

Furthermore, Xie et al (2022) experiments a combined AI and standard wireless 

communication method to traditionally test cost control after reviewing building's latest 

status beside the in the building materials (supplies) and the regulations and laws that 

actually regulate the surrounding environment and the construction process in general. 

Following experiments of the combined wireless-AI model, it has been that this model 

has gain success in both diction making in addition to the development of project cost 

management (Xie et al., 2022). in addition, applying the artificial neutral networks 

(ANN) as suggested by (Omotayo et al., 2020) prepared the floor for the creation of a 

structured decision support technique for analyzing the most appropriate Prefaricated 

Construction Component Technologies (PCCTs) deployed in at variant stages of the 

construction process. Aside from demonstrating the efficacy of the emerging ANN-

based decision support approach, the study's theoretical findings reveal that Critical 

Path Method (CPM) and Quantity Surveying (QS) professionals affect PCCTs selection 

decisions at different stages of the building process. Whereas Omotayo et al. (2020) QS 

professionals were mostly in charge of PCCT selection during the beginning and mid-

level stages, CPM professionals were in charge of PCCT selection at the building 

process close-out phase.  Elmousalami,(2020) Examine the most common 

methodologies and processes for identifying cost drivers, which have been classified 

into two categories in prior literature: quantitative and qualitative methods 



18 

(Elmousalami, 2020). In addition, the study investigates various computational 

intelligence (CI) techniques and combination approaches used in the construction of 

useful cost-predicting theories, as well as the combination of these methods in modeling 

and potential advances for expense modeling expansion, obstacles, and 

recommendations. Elmousalami (2020) examined the most widely used artificial 

intelligence (AI) techniques in cost modeling, such as fuzzy logic (FL), artificial neural 

networks (ANNs), case-based reasoning (CBR), decision trees (DT), random forests 

(RF), support vector machines (SVM), XGBoost, and genetic algorithms (GA). The 

analysis of 20 AI methods revealed that XGBoost was the most effective and reliable, 

with a mean absolute percentage error (MAPE) of 9.091% and an adjusted R² value of 

0.929 (Elmousalami, 2020).In a separate study, Cheng et al. (2009) have also employed 

AI techniques so as to predict the patterns of cash flow in construction companies. Here, 

the researchers aim to develop an effective management cash flow strategy using time 

margins, operation duration, construction space and resource requirements. They have 

employed a set of methodologies; like; K-means clustering, genetic algorithms (GA), 

fuzzy logic (FL) and the neutral networks (NN). These were also used to make analysis 

of sequential cash flow patterns whereas the results of training were used to enhance 

strategic cash flow management. In that the S-curve has a direct impact on the 

performance of project management to control cash (Cheng et al., 2009).  

1.8.4.4 Construction Site Assessment 

Abioye et al. (2021) have investigated the AI applications in the construction sector, 

aiming to assess the related methodologies, challenges and potentials, They have also 

shed light on the AI's effect on other construction concerns with regard to rather 

benefits of such technology (Abioye et al., 2021). Rampini & Re Cecconi (2022) have 

also explored the advantages of AI in managing the construction sector. To attain the 

research goals, the researchers reviewed 578 articles using bibliometric methods to 

identify leading institutions, prominent research topics, and relevant scientific journals 

(Rampini & Re Cecconi, 2022). Opting for quantitative analysis, and utilizing asset 

management (AM) and the AI technologies already implemented in these areas, they 

found that AI is widely applied in energy management, condition assessment, risk 

management, and project management (Rampini & Re Cecconi, 2022). future studies 

done by both academics and practitioners, including digital twins, generative adversarial 



19 

systems- synthetic images) for data augmentation, and deep learning for reinforcement. 

Having applied bibliometric and qualitative evaluation approach, Vein, Yin et al. (2019) 

similarly focused on the latest improvements in Building Information Modeling (BIM) 

for off-site construction (OSC). The researchers reviewed recent trends of research and 

identified knowledge gaps that might be possibly subjected to future research on BIM 

for OSC. It also contributed to the field by summarizing recent advancements and 

outlining the necessary research needs or further practice in the field of architecture, 

civil engineering, and construction (AEC) (Yin et al., 2019). Here, Patil (2019) has 

likely reviewed the use of AI in construction management, showing the significant role 

of such technology in fostering productivity in the construction sector (Patil, 2019). He 

focused on features of digitization in construction project management and the related 

sectors. Although this seems premature, comprehending the evolution of this topic, 

makes new room for further studies in the field (Patil, 2019). On the other hand, Yaseen 

et al. (2020) have developed a hybrid AI model for predicting project delays, labeled as 

Random Forest Classifier Integrated with Genetic Optimization Algorithm (RF-GA), 

(see Figure 5) below. In fact, This model resembles high performance in terms of 

classification accuracy at 91.67%, kappa value at 87%, and the error rate at 8.33%. 

87%, and an error rate of 8.33% (Yaseen et al., 2020). 

Figure 5 

Random Forest and Genetic Algorithm (RF-GA) structure 

 
Source: Yaseen et al. (2020) 



20 

Accordingly, considering control on construction projects with the due sustainability, 

this study's proposed technique indicates a high degree of efficiency as well as 

reliability in the construction project delays as suggested in (Yaseen et al., 2020). The 

latest attitudes in the engineering field indicate a growing awareness of back-end tasks 

though it is not preferable on the construction -site field tasks. As a result, typical risks 

and challenges mostly feature financial, legal, and social causes but not of interest in 

workers' physical safety regulations (Pillai & Matus, 2020).  

  



21 

Chapter Two 

Research Methodology 

Experimentally speaking, this chapter introduces the study's design with particular focus 

on data collection and processing, the construction company's sampling and the 

questionnaire's design. 

2.1 Overview 

To fill the research scarcity in the field of construction sector, this study explores the 

impact of digital transformation on the Palestinian construction companies' 

performance, particularly on the influence of digital information maturity. Such 

exploratory types of research usually aim to identify the scope of a case study or 

phenomena being investigated to clarify relevant factors or variables (Van Wyk, 2015). 

The researcher applied a bi-method approach to quantitative and qualitative data 

collection. According to Creswell(2014) this approach can provide more inclusive 

understanding of the research problem. Highlighting the present situation of digital 

transformation in the Palestinian construction companies, this study is conducted in six 

phases: 

1. Stating and outlining the problem of the study, objectives, and scope.  

2. Literature Review whereby the researcher reviews considerable previous academic 

research with due regard to this study's major trend, the influence of digital 

transformation in construction companies showing how previous researches 

addressed data collection and analysis. 

3. Data Collection: here, the researcher has employed the following approaches: 

• Qualitative Approach: whereby the researcher, interviewed Palestinian construction 

companies' employees including; executives, officials, project managers, and field 

engineers. It is worth noting here that most of the interviews were done on the phone 

due to the security instability in Palestine. 

• Quantitative Approach: A questionnaire was designed based on a review of the 

literature and the results of the interviews. 

- Data Analysis and Hypothesis Testing: Thematic analysis was used for the 

qualitative interviews (Braun & Clarke, 2006), while the questionnaire data was 

analyzed to test the relationships between the research variables. 



22 

- Developing the Theoretical Framework: A theoretical framework was developed to 

explain the relationship between digital transformation, project performance, and 

digital transformation maturity as an intervening factor. 

- Drawing Conclusions and Recommendations: Proposals were presented to enhance 

digital transformation in the Palestinian construction sector. 

The methodology followed in this research is summarized in the figure 6. 

Figure 6 

Research methodology  

 

2.2 Target population and sampling procedure 

 The study population comprised construction companies operating in the West Bank, as 

classified by the Palestinian Contractors Union for the year 2024/2025. To ensure data 

accuracy, the Palestinian Ministry of Public Works, the General Union of Palestinian 

Contractors, and the Palestinian Engineers Association were contacted to obtain a list of 

registered and active construction companies. Based on the data collected, the total 

number of active and registered companies was 333, as detailed in Table 1.  

  



23 

Table 1 

Active Registered Construction Companies 

No. Company classification Population 

1 First Classification 120 

2 Second Classification 91 

3 Third Classification 66 

4 Fourth Classification 56 

 
Total 333 

Requested sample 143 
 

Determining the minimum sample size is crucial in questionnaire-based studies and 

other statistical methods to ensure that the results can be generalized to the study 

population (SAUNDERS, 2014). This study determined the optimal sample size 

G*Power program, following the variance-based Partial Least Squares Structural 

Equation Modeling (PLS-SEM) methodology. The calculations were based on the most 

complex variable regarding the number of independent variables predicting it. In this 

case, "digital transformation adoption" was identified as the most complex variable, as it 

is influenced by three independent variables: strategy, technology, and human 

resources. 

Statistical power was analyzed using multiple linear regression analysis (fixed model, 

R² deviation from zero) Hair et al. (2019) based on the following criteria: 

• Effect size: Small (f² = 0.02), medium (f² = 0.15), and large (f² = 0.35). 

• Statistical significance level: α = 0.05. 

• Statistical power: 1 - β = 0.80. 

The results indicated that a minimum sample size of 43 participants is required to detect 

a medium effect (f² = 0.15), while a small effect (f² = 0.02) requires 119 

participants.The study targeted a minimum of 119 participants to ensure sufficient 

statistical power.  

  



24 

2.3 Questionnaire design  

For the sake of this study, the researcher has designed a questionnaire to test the study's 

hypotheses regarding digital transformation management, maturity of digital 

transformation and performance. Either of these hypotheses was assessed Liker's scale, 

namely, strong disagreement at scoring (1) or showing strong agreement upon scoring 

(5). The questionnaire's questions were built in correlation with previous studies' 

comprehensive reviews. 

The questionnaire consists of four main sections: 

Section One (6 paragraphs): addresses general information about the company and 

participants, including gender, company sector, number of employees, company 

classification, job position, and type of certificate. 

Section Two (18 paragraphs): focuses on digital transformation management through 

three main axes, which are: the strategy followed, the technology used, and human 

resources policies. 6 paragraphs were allocated to each axis to measure its role in 

supporting digital transformation. 

Section Three (6 paragraphs): measures the maturity of digital transformation in 

companies, through awareness of the importance of digital transformation, adoption of 

Building Information Modeling (BIM) technologies, integration of Internet of Things 

(IoT) technologies, adaptation to technological changes, updating strategies, and 

cybersecurity. 

Section Four (24 items): Evaluates project performance across four dimensions: time 

performance, cost performance, project quality, and project scope, with five items for 

each dimension. 

A group of experts reviewed the questionnaire to ensure the content 

validity and consistency of the questions. Their feedback regarding the length of the 

questionnaire, the wording of the questions, and the number of sentences were carefully 

considered and incorporated into the revisions. The final version of the questionnaire 

was initially written in English (see Appendix A). However, since the native language 

in Palestine is Arabic, the questionnaire was also translated into Arabic (see Appendix 

B) to ensure clarity and accessibility for all participants.  



25 

2.4 Qualitative Interviews 

The qualitative phase of the research began with conducting semi-structured interviews 

involving representatives from companies within the construction sector. A total of 13 

interviews were carried out with experts employed across nine companies, as detailed in 

Table (2). The participants comprised eight general managers, three project managers, 

and field engineers. To ensure precision in data collection and streamline the analysis 

process, all interviews were recorded using audio devices (Willis, 2015). 

Table 2 

Characteristics of the companies and role of the interviewees 

NO Company Interviewee Job role Experience (years) 

1 Company A General Manager 15 

2 Company A Project Manager 8 

3 Company B General Manager 23 

4 Company C Site Engineer 3 

5 Company D General Manager 17 

6 Company D Project Manager 12 

7 Company E General Manager 19 

8 Company F General Manager 16 

9 Company F Site Engineer 7 

10 Company G General Manager 21 

11 Company H Project Manager 9 

12 Company I General Manager 18 

13 Company L General Manager 22 
 

The data in this study were analyzed using the thematic analysis approach, a 

straightforward method that facilitates the identification, examination, and reporting of 

patterns (themes) within qualitative data (Braun & Clarke, 2006). 

  



26 

Chapter Three 

Data analysis and results 

This chapter examines and presents the findings from quantitative data gathered through 

interviews and questionnaires. The initial section evaluates companies' preparedness for 

digital transformation and its influence on project performance, drawing on insights 

provided by industry experts during the interviews. Subsequently, the results of 

descriptive statistics and hypothesis testing, conducted using the PLS-SEM and SPSS 

programs, are discussed. The primary objective of this study is to assess the current 

level of readiness for digital transformation among construction companies in Palestine 

and to determine how this readiness impacts the overall efficiency of project 

performance. 

3.1 Interviews analysis 

The primary objective of the study was to uncover key themes that reflect the current 

state of digital transformation within Palestinian companies. Additionally, it aimed to 

explore the effects of digital transformation on project performance in the construction 

sector. Based on the interview findings, the results were categorized into four central 

themes, as outlined below: 

First theme: The role of current strategy, technology, and human resources in digital 

transformation:  

Most respondents indicated the important role of current strategy, technology, and 

human resources policy in contributing to companies’ transition to digital 

transformation in projects. Most respondents indicated that the current strategy, through 

a set of mechanisms aimed at improving operational efficiency, enhancing innovation, 

and investing in data analysis technologies to improve risk prediction and decision-

making. Respondents also indicated that current technology is not just a tools, but a key 

enabler that helps companies achieve a comprehensive digital transformation in the 

construction sector. Companies can improve productivity, enhance sustainability, and 

excel in a competitive market by employing innovative technical solutions. As for the 

current human resources policy, Respondents indicated that it plays a vital role in 

supporting digital transformation in the construction sector, as it focuses on enhancing 



27 

competencies, building a supportive culture, developing flexible and comprehensive 

policies, and developing practices that contribute to the success of this transformation. 

Second theme: Companies’ readiness for digital transformation: 

It aims to assess the construction companies' readiness for digital transformation, 

crucially needed to achieve progress in a world of rapid technological development. 

Respondents insisted that technology adoption contributes to achieving efficiency, 

reducing cost, and providing customers and society with much greater value. 

Nevertheless, some respondents talk about those difficulties that hinder the Palestinian 

construction sector from turning to full adoption of digitalization. The challenges are 

mostly of political, technical, economic, regularity, and social origins.  

Third theme: Maturity of digital transformation:  

Respondents indicated that digital transformation in construction companies in Palestine 

has made significant progress, but the level of maturity varies between companies due 

to factors such as financial resources and human competencies. Some respondents from 

leading construction indicated their companies turning to adopt using digitalization 

tools; like project management software and resource planning systems (ERP). Whereas 

smaller businesses struggle hard against challenging conditions. Responses from these 

businesses reflect slow development whereby technologies like Building Information 

Modeling (BIM) and cloud computing still fall in their immature stages of adoption. 

This mostly refers to a set of factors; lack of qualified personnel, high cost, and political 

instability. Traditional corporate culture in addition to restricted governmental efforts in 

this area. Yet, their considerable initiatives are witnessed from notch the private sector 

and some international MGOs. 

Fourth theme: The Impact of Digital Transformation on Construction Project 

Performance:  

This Theme focused on the respondents' opinions and expectations about the impact of 

digital transformation on construction project performance (time, cost, quality, and 

scope), where most respondents agreed that digital transformation contributes 

significantly to reducing time and improving operational efficiency. Using digital 

planning tools such as project management software allows for improved work 

scheduling and accurate monitoring while predicting and addressing risks. 



28 

While some technological tools like 3D modeling (NIM) can reduce errors and 

reworking, digital communication platforms are eligible to accelerate the process of 

information exchange and decision-making. In fact, the construction sector's adoption of 

modern technologies like 3D-pringing and robotics helps to speed up the process of 

digital advanced transformation. AI besides advanced analytics can help attain more 

effective decision-making and better resource management, leading to achieving higher 

quality work and on-time project completion. According to most of the respondents, 

cost reduction leads to increased work efficiency. The use of Building Information 

Modeling (BIM) helps reduce errors and rework which means a real cost decrease. 

Using digital tools can also improve resource management accuracy through which 

wastes and expenses are reduced.  

The immediate instant adoption of digitalizing the construction monitory is expected to 

make a real reduction in the labor cost and long-term leases. Another technological tools 

like drones and sensors reduce much of the running and operational costs. In this regard, 

digital transformation offers solutions to have early risk analysis so that officers can 

properly manage the problems that arise and reduce financial loss in addition to 

improving asset maintenance for loner-life existence and reducing potential costs, in 

consequence. Respondents indicate how digital transformation actually contributes to 

reasonably improving the quality of construction projects through attaining accuracy 

and efficiency. This has enabled more detailed designs with better coordination between 

various disciplines. Whereas digital monitoring tools enable construction companies to 

ensure competent implementation of specifications and standards, AI could provide 

perfect analytics to improve the quality of decision-making. Here, projects can be 

virtually tested and visualized and tested with optional revising and adjusting before 

getting implemented in the real landscape. Moreover, digital communication platforms 

help make instant and accurate information with less error probability. These 

improvements have enabled construction companies to master the quality of service, 

and foster their competence to avoid project delays in response to customers' 

expectations. In conclusion, the respondents have shown that digital transformation has 

expanded the construction companies' scope of construction projects through mastering 

the eligibility to manage complicated operations with more efficient performance.  

Other digital tools like Digital modeling (BIM) are used to coordinate between different 

project aspects, allowing work expansion without violating timetables or harming levels 



29 

of quality. Such tools can also make instant monitoring and right updating to manage to 

deal with rather larger and more complicated projects. Global implementations and 

coordination might be achieved through means of digitalization regardless of the 

geographical location. Integrating modern technologies like 3D printing and robotics for 

the enhancement of ambitious and promising projects would be another advantageous 

reflection of construction projects' that transform to digitalization. More importantly, 

advanced applications of data analysis offer flexibility towards planning and preferably 

redirecting various resources. In consequence, this makes it possible to implement 

larger types of projects efficiently.  

3.2 Questionnaires analysis 

The relationship between digital transformation and construction project performance 

was examined using Smart PLS 3.2.7 and SPSS, both widely recognized tools for 

structural equation modeling (SEM) employing the partial least squares (PLS-SEM) 

approach. SEM is a multivariate analytical technique designed to assess causal 

relationships (Cho et al., 2009). SEM accordingly represents an effective pattern of 

prediction whereby small-sized samples or non-normal data distribution, Ali et al. 

(2018), are addressed with correlation to the nature of the study in hand which has 

consequently adopted the PLS-SEM-methodology for data analysis (Ali et al., 2018), 

This approach involves two key stages: (1) assessing the measurement model, which 

includes evaluating reliability and validity, and (2) analyzing the structural model, 

which entails testing hypotheses and identifying model parameters (Hair et al., 2019). 

3.2.1 Study population 

The data's demographic analysis showed that most respondents (72%) were males and 

28% were females. The majority of companies were private joint-stock companies 

(44.1%), 38.5% were family companies, and 12.6% were public joint-stock companies. 

The percentage of companies with 5-9 employees was 32.2%, while the percentage of 

companies with 10-14 employees was 23.8%, and the percentage of companies with 

more than 25 employees was 16.1%. The demographic analysis also showed that the 

companies were classified according to the Palestinian Contractors Union, where 51.7% 

of the companies were classified as first-class companies. In comparison, 19.6% of the 

companies were classified as second-class companies, and the percentages of third and 

fourth-class companies were 14.7% and 7.7%, respectively. Most of the respondents 



30 

were site engineers and general managers (39.2% and 29.5%, respectively). Most 

respondents held a bachelor’s degree (67.8%). Full details of the demographics of the 

respondents are summarized in Table 3. 

Table 3 

Demographic characteristics of the study sample 

Variable Group N (%) 

Gender 

Male 103 (72) 

Female 40 (28) 

Total 143 (100) 

Company 
classification by 
company type 

Family Company 55 (38.5) 

Public Shareholding 18 (12.6) 

Private Shareholding 63 (44.1) 

Other 7 (4.9) 

Total 143 (100) 

Number of 
employees in the 

company 

5-9 46 (32.2) 

10-14 34 (23.8) 

15-19 29 (20.3) 

20-24 11 (7.7) 

≥25 23 (16.1) 

Total 143 (100) 

Company 
classification with 

the Contractors 
Union 

1st class 74 (51.7) 

2nd class 28 (19.6) 

3rd class 21 (14.7) 

4th class 11 (7.7) 

Other 9 (6.3) 

Total 143 (100) 

Job 

General Manager/CEO 37 (25.9) 

Administrative Director 19 (13.3) 

Project Manager 28 (19.6) 

Site Engineer 56 (39.2) 

Other 3 (2.1) 

Total 143 (100) 

Educational 
degree 

Diploma 6 (4.2) 

Bachelor's 97 (67.8) 

Master's 31 (21.7) 

PhD 9 (6.3) 

Total 143 (100) 



31 

1. Multivariate Analysis (MANOVA) 

Appendix C presents the outcomes of the Multivariate Analysis, highlighting the 

statistically significant differences in the levels of dependent variables influenced by the 

independent variables. The findings are summarized as follows: 

• Gender: Statistically significant differences were observed across all dependent 

variables due to the impact of gender. 

• Company Type: Significant variations were noted in all dependent variables based on 

the type of company. 

• Number of Employees: The results demonstrated significant differences in all 

dependent variables influenced by the size of the workforce, 

• Company Classification: Statistically significant differences were identified in all 

dependent variables due to the classification of the company.  

• Job Position: Significant variations were found in all dependent variables based on 

the respondents' job positions. 

• Academic Qualification: The analysis confirmed statistically significant differences 

in all dependent variables due to variations in academic qualifications.  

3.2.2 Assessment of constructs implementation 

A general descriptive analysis was conducted to evaluate the level of ADT (Assessing 

Digital Transformation), which encompasses its key dimensions: strategy, technology, 

and human resources, in enabling digital transformation. Additionally, MP 

(Management Performance), reflecting the impact of digital transformation on 

construction project performance across its dimensions (time, cost, quality, and scope) 

was assessed. Furthermore, DM (Digital Maturity), was measured as a mediating factor 

in the relationship between digital transformation adoption and construction project 

management performance. A five-point Likert scale was utilized, where higher 

numerical values indicate greater importance, acceptance, and agreement, as illustrated 

in the table below: 

  



32 

Table 4 

Converting verbal answers according to the five-point Likert scale into numerical answers 

Scale Degree 

Responses by words Strongly 
disagree Disagree Neutral Agree Strongly 

agree 

Responses by numbers 1 2 3 4 5 
 

The responses were divided into five equal intervals to assess the response levels for the 

study items based on the established scale. These intervals were determined by dividing 

the total response range by the number of levels. Given that the study utilizes five 

levels, the following formula was applied: 

Interval length =
Upper Value of response – Lower Value of response

5
=

5 – 1

5
=

4

5
= 0.8… (1) 

Accordingly, the rating table for the response degrees was created as a standard key, so 

that, it is relied upon to estimate the response scores, as follows: 

Table 5  

Evaluating the means of response degrees 

Mean Interval Degree Evaluation 

1 → <1.80 Very low 

1.80 → <2.60 Low 

2.60 → <3.40 Moderate 

3.40 → <4.20 High 

4.20 → 5 Very high 
 

Table 6 demonstrates the total level of effects and satisfaction in a descending order. 

The descending order of impact and satisfaction levels is shown, as the results indicate 

that the overall average of the levels of adoption of digital transformation (ADT), 

digitalization maturity (DM), and management performance (MP) were 3.73, 3.64, and 

4.18, respectively, reflecting a high score in Palestinian companies. Regarding digital 

transformation adoption, the impact of technology was the most prominent with an 

average of 3.96, followed by the impact of both strategy and human resources, which 

recorded an equal average of 3.62. Digitalization maturity also showed a high level with 

an average of 3.64. As for management performance, the results showed that quality 



33 

performance recorded the highest level with a “very high” score with an average of 

4.21, while cost performance was at the minimum with an average of 4.11, highlighting 

the disparity between the different performance dimensions. 

Table 6 

Levels of ADT, DM, and MP 

Construct Mean Standard deviation Degree 

Total degree of ADT    

(Adoption of digital transformation) 3.73 0.872 High 

Impact of Technology 3.96 0.763 High 

Impact of Strategy 3.62 1.032 High 

Impact of Human Resources 3.62 0.956 High 

Total degree of DM    

(Digitalization Maturity) 3.64 0.902 High 

Total degree of MP    

(Management Performance) 4.18 0.613 High 

Quality Performance 4.21 0.636 Very high 

Scope 4.20 0.630 Very high 

Time Performance 4.19 0.696 High 

Cost Performance 4.11 0.697 High 
 

3.2.3 Assessment of the measurement model 

 The measurement assessment model is significantly based on validity and measurement 

as major factors. According to Sekaran (2003), reliability is " the degree to which a 

measurement tool yields consistent results when applied to measure a specific concept," 

while validity refers to "the quality of the instrument's design and its capacity to 

measure the intended concept accurately" (Sekaran, 2003). previous studies have shown 

that evaluating reflective measurements models intends to test internal consistency, 

reliability, convergent validity and discriminant validity (Boudreau et al., 2001; 

Saunders et al., 2016). To evaluate reflective models, researchers ac typically rely on 

the criteria developed by (Götz et al., 2010; J. F. Hair et al., 2011a) to evaluate 

reflective models. 

  



34 

• The Role of Confirmatory Factor Analysis 

Confirmatory Factor Analysis (CFA) is used to assess the relationships between 

indicators and their corresponding measurement constructs. Both reliability and validity 

are critical criteria for assessing reflective models (J. F. Hair et al., 2019). Whereas 

reliability is connected to the consistency of measurements, outcomes when the 

experiment is replicated under identical conditions, validity reflects the accuracy which 

enable instruments measure the concepts in discussion (Sekaran & Bougie, 2010). 

• Evaluation criteria 

Evaluation of reflective models includes checking: 

1. Internal Consistency. To evaluate internal consistency the researcher has applied 

both the Cronbach's Alpha and Composite Reliability (CR) which indicates strong 

reliability at (0.7.8) whereas d 0.7 scoring resemble acceptability in such exploratory 

research (Hair et al., 2011).  

2. Convergent Validity. To assess the Convergent Validity, he researcher has opted for 

the Average Variance Extracted (AVE) testing, which confirms the validity 

conceptual construct upon exceeding a rate of (0.5) to confirm the validity of the 

conceptual construct (Hair et al., 2011).  

3. Indicator Reliability. To assess the Convergent Validity, he item loading test through 

indicating the contribution of either item to the conceptual construct. Here, item 

loading is expected to exceed a value of (0.7) (J. F. Hair et al., 2019). In fact, the 

present study item loading ranges between 0.756 to 0.957, demonstrating satisfactory 

indicator reliability.  

• Main Results 

The measurement model was assessed using SmartPLS 4. The outer loadings for all 

reflective indicators were determined as shown in Figure 7. Outer loadings ranged from 

0.854 to 0.953, exceeding the recommended threshold of 0.70, indicating satisfactory 

indicator reliability. Table (7) below shows that the achieved construct indicates 

reliability indicators with CR, internal consistency at a satisfying level, it demonstrates 

that the selected items are effective in measuring the three main dimensions (strategy, 

technology, human resources) in addition to performance and maturity. 



35 

Figure 7 

The results of PLS -algorithm for research model 

 

• Validity Analysis 

The researcher has examined convergent validity through testing positive relationships 

between a construct's alternative indicators, while discriminant validity is tested on 

comparisons with different variables. Here, the calculation of the Average Variance 

Extracted (AVE) confirmed that the model assures the convergent validity condition, 

with all values surpassing (0.5). Such findings demonstrate that the reflective model 

exhibits strong validity and reliability, also confirmed in the internal consistency, 

indicator reliability, and both convergent and discriminant validity. This also indicated 

the validity of the assessment procedure implemented.  

 Table 7 

Results of reliability and validity analysis 

 

Cronbach's 
alpha 

Composite 
reliability (rho_a) 

Composite 
reliability (rho_c) 

Average variance 
extracted (AVE) 

Cst 0.903 0.905 0.940 0.838 

DM 0.935 0.937 0.954 0.838 

Hr 0.933 0.934 0.958 0.883 

MP 0.925 0.928 0.947 0.816 

Qty 0.927 0.927 0.948 0.821 

Scp 0.921 0.923 0.944 0.809 

Str 0.894 0.895 0.950 0.904 

Tch 0.932 0.933 0.948 0.785 

Time 0.861 0.862 0.915 0.783 



36 

Table (7) above has shown that all (AVE) constructs confirm validity via exceeding the 

minimum value of (0.5). it also indicates that variant contract indicator really 

contributes to each other's. 

• Convergent Validity: 

Discriminant validity is defined by  Hair et al., (2014) as the ability of standardized 

instruments to differentiate between different constructs or measure disparate concepts. 

In contrast to convergent validity, discriminant validity assesses each measure based on 

its independent properties (Campbell & Fiske, 1959). This validity plays a crucial role 

in ensuring that research instruments do not measure unexpected or irrelevant items 

(Urbach&Ahlemann, 2010).  

Two primary methods are employed to assess discriminant validity: cross-loading 

(Chin, 1998) and (Fornell & Larcker, 1981). According to Hair et al. (2011), 

discriminant validity is established when the average variance extracted (AVE) for each 

latent construct exceeds its highest squared correlation coefficient with other latent 

constructs (Hair et al., 2011), as per the Fornell-Larcker (1981). Additionally, indicator 

loadings must be greater than any cross-loadings to ensure the validity of the 

measurement. This study evaluated discriminant validity using the Fornell-Larcker 

Criterion (Fornell & Larcker, 1981). 

As illustrated in Table 8, the diagonal values in the correlation matrix represent the 

square root of the average variance extracted (AVE) for each latent construct. 

Discriminant validity is achieved when the diagonal values surpass the non-diagonal 

values within the matrix, a condition reflected in the correlation matrix results, thereby 

enhancing the reliability of the study's discriminant validity.  

Additionally, the correlation matrix of the independent variables was examined to 

identify any indications of strong associations between them. Multicollinearity occurs 

when the correlation coefficient between the independent variables exceeds 0.9, 

according to both Hair et al. (2010) and Pallant (2016). Conversely, Pallant (2016) 

suggests that a correlation coefficient higher than 0.7 may indicate the presence of 

multicollinearity (Hair et al., 2010; Pallant, 2016). 



37 

Moreover, discriminant validity was verified using the heterotrait-monotrait ratio 

(HTMT) (Henseler et al., 2015). As shown in Table 9, all HTMT values were below the 

most conservative threshold of 0.85, indicating no strong associations between the 

external factors. The results confirmed that all correlation values were well below this 

threshold, indicating no issues related to multicollinearity and reinforcing the quality of 

the study's discriminant validity.  

Table 8 

Discriminant validity check (square root of AVE is shown on the diagonal in bold) based on 

Fornell-Larcker criterion method 

 
Cst DM Hr Qty Scp Str Tch Time 

ADT 
        

Cst 0.916 
       

DM 0.522 0.915 
      

Hr 0.397 0.809 0.939 
     

Qty 0.737 0.583 0.5 0.906 
    

Scp 0.746 0.503 0.455 0.823 0.9 
   

Str 0.391 0.782 0.803 0.456 0.387 0.951 
  

Tch 0.543 0.781 0.829 0.606 0.575 0.788 0.886 
 

Time 0.774 0.473 0.415 0.706 0.743 0.412 0.569 0.885 

 

Table 9 

Discriminant validity check using HTMT 

 Cst DM Hr Qty Scp Str Tch Time 

Cst         

DM 0.569        

Hr 0.432 0.865       

Qty 0.804 0.627 0.536      

Scp 0.814 0.54 0.489 0.89     

Str 0.434 0.853 0.879 0.499 0.423    

Tch 0.593 0.832 0.887 0.652 0.619 0.861   

Time 0.876 0.528 0.465 0.791 0.831 0.468 0.638  

 

  



38 

• The Variance Inflationfactor (VIF) 

Based on the disjoint two-stage approach, the assessment of the formative second-order 

construct ADT demonstrates satisfactory measurement properties. The outer weights 

indicate that the Impact of Technology (Tch) (0.623) makes the strongest contribution 

to ADT, followed by the Impact of Human Resources (Hr) (0.257) and the Impact of 

Strategy (Str) (0.178). All first-order constructs exhibit high outer loadings—0.875 for 

Str, 0.977 for Tch, and 0.917 for Hr—exceeding the recommended threshold of 0.70 

(Sarstedt et al., 2021), confirming strong indicator reliability. Moreover, the VIF values 

(3.697 for Tch and 3.942 for Hr) are well below the conservative cut-off of 5 (Sarstedt 

et al., 2021), indicating the absence of multicollinearity issues among the indicators. 

These results collectively support the validity of the ADT construct as a second-order 

formative construct. 

Table 10 

Assessment of formative constructs. 

Second -order 
construct 

First -order construct 
Outer 

weights 
Outer 

loadings 
VIF 

ADT 

Impact of Strategy (Str) 0.178 0.875 3.25 

Impact of Technology (Tch) 0.623 0.977 3.697 

Impact of Human Resources 
(Hr) 

0.257 0.917 3.942 
 

• Model fit  

The model fit indices indicate that the proposed model demonstrates an excellent fit to 

the data. The SRMR value is 0.049, which is below the commonly recommended 

threshold of 0.08 (Hu & Bentler, 2009). The Chi-square statistic (144.160) is reported 

alongside a high NFI value of 0.910, which exceeds the recommended cut-off of 0.90 

(Bentler & Bonett, 1980; Sarstedt et al., 2021)., further confirming the adequacy of the 

model. Overall, these results provide strong evidence that the structural model fits the 

data well. Appendix D show the model fit  

  



39 

3.2.4 Assessment of the structural model 

PLS analysis was employed to evaluate the internal structure of the model, following 

the criteria established by Hair et al. (2011), Hair, Jr et al. (2013), and Fernandes 

(2012). This evaluation encompassed an analysis of R² values, effect size (f²), the 

predictive significance of the model, and goodness of fit (GoF) (Fernandes, 2012; Hair, 

Jr et al., 2013; J. F. Hair et al., 2011). Finally, the study has examined the magnitude 

and significance coefficients. These have been retested for the sake of the study 

hypotheses validity.  

3.2.4.1 The determination coefficient (R²) 

Hair et al. (2011) introduce several criteria for the (PLS-SEM) model assessment, 

including; (i) coefficients of determination (R²), (ii) as well as the significance and (iii) 

magnitude of the path coefficients, besides the effect size (f²), predictive significance 

(Q²), and effect size (q²). The criteria represent the accuracy of the path model in PLS at 

a high level of the addressed constructs' (R²). Grading R² into 'high' level depends on the 

field of research; while an (R²) of (0.75) indicates a high explanatory power, (R²) at 

(0.20) seems high with due regard to customer behavior (Hair et al., 2011). Yet, in 

marketing research, R² values are classified into; 0.75 = Strong effect, 0.50 = Medium 

effect, and 0.25 = Weak effect (J. F. Hair et al., 2019). 

Consequently, this indicates the significance of (R²) as a fundamental indicator for 

measuring the extent to which external variables explain the variance in the endogenous 

variables, being also vital the quality of the structural model assessment. Appendix E 

shows that the R² value for the MP variable at 0.695, indicating a high level of 

explanation. It means the ADT and DM variables explain 69.5% of the variance in MP. 

3.2.4.2 The determination coefficient (R²) 

The effect size analysis (f²) vitally estimate the amount of influence t latent variable has 

one the dependent ones(Chin & Newsted, 1998). the following formally is actually used 

to estimate the value (f²) as PLS analysis does not automatically compute it: 

Effect size: �� =
�������� 

� ��������� 
�

����������
�   ……………………. (2) 



40 

This type of analysis followed Cohen's(1988) model of manual calculation whereby (f²) 

values' size effects are categorized into either small at 0.02, medium at 0.15 or large at 

0.35 predictive variables (Cohen, 1988). So, the present study can apply such model to 

evaluate the role interactive variables in addition to utilizing this approach in PLS 

analysis (Landau & Bock, 2013). For testing the model and having an accurate 

understanding the effect of of interactive factors on structural relationships, Hair et al. 

(2013) and Henseler & Fassott (2010) have recommended turning main effects into 

simple or single ones see(Hair, Jr et al., 2013; Henseler & Fassott, 2010). As shown in 

Appendix E, the f² values for both ADT and DM in explaining the endogenous variable 

MP were 0.079 and 0.030, respectively, indicating that the effect size of ADT and DM 

on MP is classified as large. 

3.2.4.3 Predictive Relevance of the Model 

Assessing the strength of a structural model is a fundamental process that involves using 

a “blindfold” technique to create an iterative cross-validation measure Q², in parallel 

with analyzing the coefficients of determination R2 and effect sizes. According to Hair 

et al. (2011), assessing Q² for both structural and standard models requires predicting 

the data using PLS-SEM estimates, which is consistent with the methodology adopted 

for this analysis(Hair et al., 2011). A Q² measure greater than zero for any endogenous 

latent variable indicates high explanatory power for that variable. The Q² measure is 

utilized to assess how effectively the model predicts data that were not included in the 

estimation process, a concept known as predictive relevance  (Hair, Jr et al., 2013). 

According to  Fernandes (2012), the Stone-Geiser test is calculated using the following 

equation: 

�� = 1 − !!"/!!$ ………………….. (3) 

It is important to note that the blindfolding technique is considered appropriate when the 

target latent variables consist of reflective indicators (J. F. Hair et al., 2011; Henseler et 

al., 2009). Bagozzi (1994) explained that a positive Q² value indicates that the model 

has predictive relevance, while negative values suggest a lack of predictive ability 

(Bagozzi, 1994). Appendix E shows that the Q² values for MP and DM were (0.349) 

and (0.682), respectively, approving the model's strong predictive fitting.  

  



41 

3.3 Testing of Hypotheses 

The present study aims to examine the effect of ADT practices on the MP four 

dimensions, with regard mediating role of DM. So, to determine the strength of the 

relationships between the latent variables, the standardized path coefficients (β), the 

they applied the PLS algorithm with a default setting of 300 iterations using factor 

analysis as a weighting method. Ranging between (-1- +1), β values in coefficient get 

even stronger when approaching (±1) (J. F. Hair et al., 2019). Figure (8) illustrates he t-

value was determined through the bootstrapping procedure hypotheses and assess the 

significance o variables relations, the t-value was determined through bootstrapping 

procedure with a default setting of 500 subsamples. here, a relationship becomes such 

significant on statistic if the t-value signs 1.96 at 5% significant level while the P-value 

is less than 0.05. see figure (8) and Appendix F.  

The results demonstrated that ADT practices have a positive and statistically significant 

effect on MP, with values of β = 0.399, t = 3.182, and p = 0.001, supporting hypothesis 

H1. These findings align with the results of studies by Naji et al. (2024) and Dolla et al. 

(2023). The analysis also revealed a statistically significant positive relationship 

between ADT and DM, with values of β = 0.834, t = 32.341, and p = 0.00, supporting 

hypothesis H2. This is consistent with the findings of Wernicke et al. (2023), who 

confirmed the existence of a positive relationship between ADT and DM. 

Additionally, a statistically significant positive effect was found between DM and MP, 

with values of β = 0.399, t = 3.182, and p = 0.001, supporting hypothesis H3. These 

results are in line with the study by Wernicke et al. (2023), which indicated that DM 

positively influences the performance of construction projects. 

  



42 

Figure 8 

PLS Bootstrapping (T-values) for the research model 

 

Mediating test 

DM serves as a variable of mediating between ADT and MP contributing to formulate 

hypothesis H4. As for methodology, the researcher has adopted the model of Preacher 

and Hayes (2008), which accordingly distinguish between direct and indirect effects 

(Preacher & Hayes, 2008). 

Analysis has revealed that ADT's direct effect on MP, excluding mediating variables, is 

statistically significant with the values as β = 0.399, t = 3.182, and p = 0.001. Upon 

including mediating variables, the effect of ADT on MP becomes like; β = 604, t = 

10.911, and p = 0.000 reflecting that the mediating variables have considerable effect of 

ADT on MP.  

Regarding the indirect effect (ADT → DM → MP), analysis has shown statistical 

significance, with values of β = 0.205, t = 2.110, and p = 0.035. To determine the extent 

of such effect, Hair et al. (2019) has recommended calculating the variants (VAF) ratio, 

which measures the proportion of the indirect effect (Hair et al., 2019). Yet, this study 

has revealed that the VAF, falling between 20%-80% is 54.55 to indicate partial 

mediation according to the criteria of Hair et al. (2019). Accordingly, it is evident that 

DM partially mediates the relationship between ADT and MP, thereby supporting 

hypothesis H4.  



43 

Chapter Four 

Discussion and conclusions 

4.1 Chapter overview 

This chapter discusses the present study's findings and data analysis including 

descriptive statistics, hypotheses testing, and interviews. It has two sections whereby: 

the first examines the role of digital transformation in the construction projects' 

performance with due regard to the effective factors; of strategy, technology, and human 

resources, and the second summarizes the main findings and conclusions of the study.  

4.2 Discussion 

This present study examines the roles of strategy, technology, and human resources in 

the process of