The Future of Construction: Integrating Innovative Technologies for Smarter Project Management

Abstract

The construction industry is transforming significantly, with emerging technologies reshaping project management by enhancing efficiency, sustainability, and safety. This study examines the integration of these innovations into Chad’s construction sector, drawing on insights from 79 industry participants. Given Chad’s unique economic and infrastructural landscape, understanding the practical implementation of these technologies is crucial. This research demonstrated strong reliability and validity through exploratory factor analysis, with a KMO value above 0.75, statistical significance at p < 0.001, and a Cronbach’s Alpha exceeding 0.8. Using Promax rotation, this study identified 15 key factors, providing valuable insights into how technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology are transforming construction processes. These tools enhance design accuracy, facilitate real-time decision-making, and minimize material waste while supporting global sustainability goals, including the United Nations’ Sustainable Development Goals (SDGs). Examining the adoption of these technologies within Chad is particularly important, as the country faces unique challenges that demand tailored solutions. While digital transformation in the construction industry has been widely studied worldwide and in Africa, Chad’s industry remains relatively unexplored in this regard. This research bridges this gap by identifying both the opportunities and the barriers to technological integration in the sector. Embracing these innovations could help modernize Chad’s construction industry, addressing persistent inefficiencies and promoting environmental sustainability. However, widespread adoption is hindered by significant challenges, including high implementation costs, limited access to advanced tools, and a shortage of skilled professionals. Overcoming these obstacles will require strategic investments in education, infrastructure, and supportive policies. By fully leveraging technological advancements, Chad has the potential to build a more competitive, resilient, and sustainable construction industry, driving national development while aligning with global sustainability initiatives.

1. Introduction

1.1. Integrating Innovative Technologies

The construction industry is undergoing a remarkable transformation, driven by the integration of cutting-edge technologies that are reshaping how projects are managed, executed, and maintained on both national and global scales. These advancements are unlocking new levels of efficiency, enhancing safety, and promoting sustainability, addressing many of the long-standing challenges the sector faces. Tools like Building Information Modeling (BIM) revolutionize collaboration, providing precise 3D models that streamline planning and decision-making. Artificial Intelligence (AI) and machine learning are optimizing workflows, offering predictive insights to manage risks, allocate resources, and prevent costly delays [1,2]. Robotics and automation are stepping in to perform repetitive and high-risk tasks, alleviating labor shortages and improving workplace safety. Meanwhile, drones and IoT-enabled devices enable real-time monitoring and site analysis, fostering more competent project oversight [3].

Technologies like virtual and augmented reality (VR and AR) redefine project visualization, enabling stakeholders to immerse themselves in designs and construction teams to execute tasks more accurately. Prefabrication and modular construction methods are cutting down timelines, reducing material waste, and improving the consistency of finished projects [4]. As sustainability becomes a critical focus, eco-friendly materials and energy-efficient practices are gaining traction, paving the way for greener construction. Additionally, innovations like Digital Twin technology and blockchain are introducing new levels of transparency, predictive capabilities, and operational efficiency [5,6]. From self-healing concrete to 3D printing, advanced materials are extending the lifespan of structures while minimizing resource consumption [7,8,9].

Together, these technologies are not just addressing inefficiencies but setting new benchmarks for conceptualizing and realizing infrastructure. Adopting these tools is reshaping the construction landscape, enabling faster, safer, and more cost-effective projects that align with the demands of a rapidly evolving world.

1.2. Smart Technologies for Sustainable Construction

Innovative technologies are revolutionizing the construction industry, driving sustainability, addressing environmental challenges, and raising social consciousness [10,11]. Among the most impactful innovations is Building Information Modeling (BIM), a powerful digital tool that enables architects and engineers to develop precise, data-driven 3D models [12]. This technology enhances energy efficiency, reduces material waste, and optimizes a building’s entire lifecycle. Similarly, the Internet of Things (IoT) plays a pivotal role in sustainable construction by enabling real-time tracking of energy consumption, air quality, and water usage [13]. This data-driven approach helps streamline maintenance strategies and significantly reduces resource wastage.

Another game-changing advancement is prefabrication and modular construction, where building components are precisely manufactured off-site, reducing material waste, speeding up assembly, and lowering on-site emissions [14]. Additionally, self-healing concrete, which contains bacteria that automatically seal cracks, extends the lifespan of structures while minimizing the need for resource-heavy repairs. Meanwhile, Artificial Intelligence (AI) and machine learning are being utilized to process vast amounts of data, fine-tune energy efficiency, anticipate structural weaknesses, and enhance material selection to reduce environmental impact [15,16].

The integration of renewable energy solutions is another cornerstone of sustainable construction. Bright solar panels, energy-efficient HVAC systems, and dynamic glass windows are helping buildings significantly cut energy consumption. Likewise, 3D printing technology is reshaping the industry by enabling rapid, cost-effective construction using recycled materials, reducing labor demands and carbon footprints. Moreover, green roofs and intelligent irrigation systems contribute to sustainability by improving insulation and promoting efficient water management [17].

The construction industry in Chad faces numerous challenges, including inefficient resource utilization, a shortage of skilled labor, high costs associated with adopting cutting-edge technology, and a regulatory framework that remains largely underdeveloped. These obstacles have significantly slowed the implementation of digital solutions that could improve project management efficiency and promote sustainability.

This study presents a distinctive perspective by examining how integrating Building Information Modeling (BIM), Artificial Intelligence (AI), and the Internet of Things (IoT) can help address these challenges within Chad’s construction sector. Unlike previous research, which has primarily examined technological adoption across Africa as a whole, this study focuses specifically on the unique barriers and opportunities present in Chad. By addressing the country’s specific constraints, this research provides valuable insights into how innovative technologies can be leveraged to drive efficiency, reduce costs, and support sustainable development in the local construction industry.

1.3. Challenges and Best Practices for Adopting New Technologies

The integration of innovative technologies holds immense potential for transforming various industries, particularly the construction sector [18]. However, despite the numerous benefits, such as increased efficiency, enhanced sustainability, and improved project management, several obstacles hinder widespread adoption. One of the most significant challenges is the high upfront cost associated with acquiring, implementing, and maintaining advanced tools, such as Building Information Modeling (BIM), drones, Artificial Intelligence (AI), and the Internet of Things (IoT) [19]. This issue is particularly pronounced in developing nations like Chad, where limited access to financial resources makes large-scale investments difficult.

Another major hurdle is the skills gap, as many professionals lack the technical expertise to operate and maximize the potential of these cutting-edge technologies [20]. This challenge is further compounded in regions where specialized training programs focused on emerging construction innovations are scarce. Infrastructure limitations such as unreliable electricity, inadequate internet access, and outdated equipment also create significant barriers, making the seamless integration of new technologies a daunting task [21]. Additionally, compatibility issues with existing systems often complicate adoption, leading to potential workflow disruptions [22].

Beyond technical and financial challenges, cultural resistance to change remains a critical issue [23]. Many industry stakeholders are reluctant to abandon familiar, traditional methods due to skepticism about the return on investment or concerns about job displacement. Regulatory shortcomings further slow progress, as unclear policies and a lack of government-driven innovation initiatives create uncertainty. Furthermore, import restrictions, high tariffs, and logistical constraints, particularly in rural areas, make it even more difficult to access modern construction technologies. At the same time, cybersecurity threats increase with digital transformation, necessitating robust data protection measures [24].

To successfully navigate these challenges, businesses and policymakers must implement strategic solutions. Investing in education and workforce training can bridge the skills gap, while gradual, phased implementation of new technologies allows for a smoother transition and minimizes risks [25]. Strengthening cybersecurity protocols, including encryption and regular audits, is crucial for safeguarding sensitive data. Additionally, government incentives, international collaborations, and funding initiatives can play an essential role in enhancing infrastructure, expanding financial access, and promoting the adoption of technology [26]. By embracing these best practices, the construction industry, especially in countries like Chad, can effectively integrate modern innovations, boost productivity, and drive sustainable development.

1.4. The Role of Innovative Technologies in Sustainable Construction Management

The construction industry is undergoing a significant shift as advanced technologies redefine project management, improving efficiency, precision, and sustainability [27]. These innovations not only streamline operations but also align with the United Nations’ Sustainable Development Goals (SDGs), a key global objective for sustainability [28]. By incorporating tools such as Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology, construction companies are optimizing resource utilization, reducing environmental impact, and enhancing on-site safety [29].

Technological advancements have become a key factor in the success of construction projects, particularly in managing costs, adhering to schedules, and maintaining quality standards [29,30]. BIM facilitates real-time collaboration among stakeholders, ensuring better design coordination and early detection of potential problems. Meanwhile, AI and machine learning process vast amounts of data to refine decision-making, anticipate risks, and allocate resources more effectively, helping projects stay within budget and on schedule [31,32]. Various factors, including financial constraints, regulatory compliance, workforce availability, and environmental considerations, influence decision-making in construction. Cloud-based project management solutions offer automation, predictive analytics, and real-time access to critical data, enabling project managers to make informed and strategic choices. Simultaneously, IoT-powered smart sensors and drones continuously monitor construction sites, improving efficiency and oversight while reducing risks [33].

Beyond optimizing workflows, specialized project management software such as Procore, Autodesk Construction Cloud, and Primavera P6 has revolutionized the way projects are tracked, documented, and managed. These platforms centralize project data, reducing miscommunication and improving collaboration. Their effectiveness is primarily determined by their scalability, ease of integration, user-friendliness, and cost efficiency. With customizable dashboards and detailed reporting tools, managers can track key performance indicators with greater accuracy, resulting in more streamlined and effective project oversight [34].

In addition to boosting operational efficiency, technological advancements are playing a pivotal role in promoting sustainability within the construction sector [35]. These innovations align with several SDGs, including Industry Innovation and Infrastructure (SDG 9), Sustainable Cities and Communities (SDG 11), and Responsible Consumption and Production (SDG 12) [36]. The adoption of eco-friendly materials such as recycled concrete, bio-based composites, and self-healing concrete has contributed significantly to reducing waste and conserving resources. Meanwhile, 3D printing is revolutionizing construction by lowering costs, decreasing labor dependency, and enabling sustainable architectural solutions [37]. IoT-integrated smart buildings further support energy conservation by leveraging automated lighting, intelligent climate control, and real-time energy monitoring, aligning with the objectives of Sustainable Development Goal 7 (SDG 7): Affordable and Clean Energy [38].

Efforts to mitigate environmental impact are also being strengthened through BIM and Geographic Information Systems (GISs), which help construction teams assess and minimize their ecological footprint [39]. Green infrastructure elements such as rooftop gardens, permeable pavements, and rainwater harvesting systems play a crucial role in combating climate change while preserving biodiversity, contributing to Climate Action (SDG 13) and Environmental Conservation (SDGs 14 and 15). From a social and economic perspective, workforce inclusivity and job creation, as emphasized in SDGs 5, 8, and 10, are being reinforced through gender-inclusive hiring practices and automation, which enhance productivity without displacing workers [40]. Additionally, AI-driven data analysis and blockchain-based smart contracts strengthen transparency, accountability, and regulatory adherence, supporting Peace, Justice, and Strong Institutions (SDG 16). Public–private partnerships (SDG 17) are further driving innovation by fostering collaboration in research, development, and workforce training initiatives [41].

Technology has also become instrumental in improving safety on construction sites, significantly reducing workplace accidents and ensuring compliance with occupational health and safety standards. Wearable smart devices, including helmet-mounted sensors and vests equipped with biometric tracking capabilities, enable the real-time monitoring of worker health and environmental conditions, facilitating the detection and prevention of hazards before they escalate [42]. AI-driven risk assessment tools analyze past incidents to predict potential safety threats, enabling the implementation of proactive measures to mitigate risks. Virtual reality (VR) and augmented reality (AR) have introduced immersive safety training programs, allowing workers to familiarize themselves with hazardous conditions in a controlled environment before entering the job site. Additionally, drones have become an indispensable tool for conducting site inspections, reducing the need for manual checks in dangerous areas while improving overall project oversight [43,44].

As the industry evolves, emerging technologies such as blockchain, robotics, and autonomous construction equipment are poised to transform project management and execution further. Blockchain technology enhances contract transparency and fraud prevention, while robotics and automation are revolutionizing construction processes by increasing accuracy, minimizing waste, and reducing reliance on manual labor [45]. By adopting these groundbreaking technologies, construction firms can enhance efficiency, reduce costs, improve worker safety, and contribute to a more sustainable and resilient built environment. Integrating innovative solutions is no longer a luxury but a necessity, driving the construction industry toward a smarter, safer, and more environmentally responsible future.

1.5. Chad’s Construction Industry

Chad’s construction industry faces numerous challenges that hamper its growth, efficiency, and long-term sustainability. One of the most pressing issues is the steep cost of building materials, primarily driven by a heavy dependence on imports, insufficient local production, and inefficiencies within the supply chain [46]. These difficulties are further compounded by inadequate infrastructure, including poorly maintained roads and an unreliable electricity supply, both of which complicate project execution and inflate costs [47]. Additionally, the sector struggles with a severe shortage of skilled labor, as vocational training programs remain underdeveloped, forcing companies to rely on foreign expertise. Moreover, bureaucratic inefficiencies, weak regulatory enforcement, and corruption contribute to frequent project delays and budget overruns [48,49]. The industry’s lack of digitalization exacerbates these inefficiencies, making it challenging to monitor progress, allocate resources efficiently, and maintain transparency.

Despite these hurdles, the construction sector in Chad holds significant potential for transformation by integrating advanced digital technologies. Implementing Building Information Modeling (BIM) could enhance project planning and coordination, reducing waste and optimizing resource management [50,51]. The use of drones and Geographic Information Systems (GISs) would significantly enhance land surveying and site supervision, while automation and prefabrication techniques could streamline construction processes and reduce project timelines [52]. Furthermore, mobile and cloud-based project management tools would facilitate seamless communication and documentation, minimizing errors and unnecessary delays [53]. Blockchain technology also presents an opportunity to strengthen contract management and financial transparency, helping to combat corruption-related issues [54].

Embracing these cutting-edge innovations would yield multiple benefits, including increased efficiency, cost reduction, improved quality control, and enhanced safety standards. With rapid urbanization and ongoing infrastructure development supported by government initiatives and foreign investments, there is an urgent need to modernize construction practices. By adopting digital technologies, Chad can establish a more competitive and resilient construction industry, ultimately fostering economic growth and sustainable development.

Although the adoption of innovative construction technologies has been extensively researched worldwide, including within Africa, exploring how these advancements can be effectively implemented in Chad’s unique context remains crucial. The country’s construction industry faces structural, economic, and regulatory challenges requiring tailored digital solutions. A study focused on Chad will help uncover the most practical strategies for overcoming local obstacles to technology adoption while contributing to the broader discourse on digitalization in construction in developing economies. By distinguishing itself from existing research, this study will provide valuable insights into the feasibility, advantages, and potential challenges of implementing innovative technologies in Chad’s construction sector, highlighting the pressing need for digital transformation.

1.6. Objective of This Study

This article examines the impact of integrating innovative technologies within Chad’s construction industry. As the global construction sector continues to embrace advanced tools and methodologies to boost efficiency, sustainability, and overall project outcomes, it becomes crucial to understand how these technologies are adopted in Chad, where challenges such as limited resources and underdeveloped infrastructure can hinder progress.

This study focuses on understanding the effects of these technologies on various stakeholders within the construction industry in Chad, including contractors, engineers, laborers, and project managers. It examines how these tools impact daily operations, decision-making processes, and skill development, highlighting both the opportunities and challenges they present.

Additionally, this article examines how these technological advancements align with the United Nations’ Sustainable Development Goals (SDGs), specifically in promoting environmentally friendly, efficient, and economically viable construction practices. Innovative technologies have the potential to reshape Chad’s construction sector by promoting more sustainable building methods, reducing environmental impact, and enhancing resource efficiency.

By achieving these objectives, this article seeks to provide valuable insights for policymakers, industry professionals, and international organizations. It offers practical recommendations for overcoming current barriers and leveraging technology to foster sustainable development in Chad’s construction industry.

This article is structured as follows: Section 2 outlines this study’s materials and methodology. Section 3 presents the research findings. In Section 4, the results are analyzed and discussed in depth, offering key insights. Finally, Section 5 concludes this article, emphasizing the significant impact of innovative technologies on construction project management. This section also highlights how these technologies contribute to more sustainable projects and promote brighter, more efficient management approaches within the construction industry.

2. Materials and Methods

2.1. Research Design

This study examines the impact of integrating innovative technologies into construction project management in Chad, with a focus on their contribution to achieving the Sustainable Development Goals. A quantitative approach was employed in this study, allowing for a rigorous analysis of the collected data.

The first stage of this research involved a literature review, which provided a theoretical and contextual framework essential for understanding the dynamics of digitalization in the construction sector. This foundation facilitated the design of an online questionnaire to capture the perceptions and experiences of key stakeholders, including companies, engineers, architects, and other professionals.

The online data collection method, particularly suited to the post-COVID-19 context, was chosen for its efficiency and ability to reach a wide range of respondents. Digital tools, now indispensable, greatly facilitated the distribution and return of the questionnaires.

The study employed exploratory factor analysis (EFA), explicitly using the principal axis factoring (PAF) method to analyze the data. This technique identified underlying patterns within the responses, providing a deeper insight into how innovative technologies impact construction project management in Chad. This study enhances the understanding of key technological impacts on the industry by uncovering these latent structures.

The research findings offer valuable recommendations for modernizing Chad’s construction sector, paving the way for more efficient and sustainable practices. The choice of principal axis factoring (PAF) over other EFA methods was deliberate, as PAF is particularly effective in identifying latent variables within smaller datasets while emphasizing common variance [55]. This makes it an ideal approach for extracting meaningful insights in contexts where data may be limited but still hold significant analytical value (Figure 1).

2.2. Population and Sampling Techniques

In research, sampling techniques are crucial as they enable the study of a representative subset of a population, thereby saving time and resources. Nabila Amir et al. [56] presented an overview of sampling methods, highlighting their importance in decision-making and data analysis across various fields. There are two main categories of sampling techniques, probability and nonprobability, which impact the quality and validity of researchers’ findings.

In Rahi’s [57] work, he defines a population as the set or group of all individuals, entities, or items one seeks to understand. In this context, architects and engineers officially registered with the Order of Architects and Engineers of Chad are the primary targets of this study.

This study primarily examines the role of architects and engineers, as they are key players in adopting technology within Chad’s construction industry. Their decisions play a crucial role in shaping the integration of innovative solutions, making them the primary focus of this research. However, this study also acknowledges the contributions of other professionals, including contractors and project managers, who play a role in the decision-making process. While their perspectives are not the central emphasis, their influence on technology implementation will be noted in the limitations section. This approach ensures a well-rounded perspective on the construction ecosystem while maintaining a focused analysis of the industry’s most influential stakeholders driving technological advancements.

The construction industry in Chad comprises tens of thousands of engineers and architects, most of whom have historically operated informally within the sector. This informal practice has made it difficult, if not impossible, to identify and formalize their professional roles. To address this issue, the Order of Architects and Engineers of Chad was established approximately five years ago, providing a sustainable framework and supervision for engineering and architecture professions. Today, around 150 architects and 200 engineers are registered with their respective orders, marking significant progress toward professional regulation within the industry.

Given the sector’s size, the challenges of managing it, and the constraints related to data accessibility, studying the entire industry was impractical. Consequently, this research focuses solely on engineers and architects who are officially registered with the order, adopting a more targeted and manageable approach to analyzing the regulated sector.

In exploratory factor analysis (EFA), sample size has been a widely debated issue for decades, as inadequate sample sizes have frequently hindered the practical application of this method [58]. The literature on factor analysis offers a range of guidelines for determining the optimal sample size to achieve reliable results. Most of these recommendations emphasize the importance of large sample sizes, often suggesting a minimum of 200 observations to ensure the accuracy and robustness of factor analysis solutions. However, in social and behavioral research, it is common to encounter datasets with smaller sample sizes, making it challenging to adhere to these established standards [59]. In many situations, expanding the sample size is not feasible. This is particularly true in medical research, where gathering a large cohort of patients with a specific condition can be difficult. For example, exploratory factor analysis (EFA) was conducted on MRI-derived cortical and subcortical brain volume measurements from a limited sample of 44 individuals diagnosed with schizophrenia [60]. In management research, sampling units typically consist of firms or products, making it challenging to obtain large sample sizes. As a result, researchers often face limitations in data availability, which can impact the scope and generalizability of their findings [58].

To ensure the validity of the quantitative survey, it was essential to obtain a sufficient number of responses to the questionnaire. Out of the 300 online surveys distributed to key industry decision-makers, 79 responses were collected. While this may seem like a relatively low response rate, it remains sufficient for meaningful data analysis and developing well-founded conclusions. The primary aim was to gather insights that accurately represent the experiences and viewpoints of professionals within the sector.

Although 79 responses might appear limited, existing research indicates that factor analysis can still be effectively conducted with smaller datasets if the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s Test confirm strong sampling adequacy. In this study, a KMO value of 0.81 underscores the dataset’s robustness, reinforcing the findings’ reliability.

2.3. Survey Design and Data Analysis

A comprehensive 5-point Likert scale questionnaire was designed to gather valuable insights from respondents. It was organized into three main sections to ensure a clear and focused approach.

The first section collected information about the organization and respondents, including their roles, areas of expertise, and work environments. This provided essential context and a foundational understanding of the participants.

The second section examined key topics pertinent to this study, including the adoption of innovative technologies, sustainability in construction, and the challenges and solutions associated with achieving project success. It also examined decision-making factors, environmental and social impact assessments, and trends in emerging technologies. As presented in

Table 1. Key areas and SDGs.

Section	Topic	Number of Statements	References
Key Topics
1	Integration of Innovative Technologies (IIT)	7	[61,62,63,64]
2	Sustainability in Construction (SC)	6	[65,66,67,68]
3	Challenges and Solutions (CSs)	7	[62,69,70,71,72,73,74]
4	Correlation with Project Success (CPS)	7	[75,76,77,78,79,80]
5	Decision-Making Factors (DMFs)	7	[61,81,82,83,84]
6	Evaluation of Project Management Software (EPMS)	7	[65,83,85,86,87,88]
7	Safety and Risk Management Improvement (SRMI)	7	[89,90,91,92]
8	Environmental and Social Impact Assessment (ESIA)	7	[62,65,93,94,95,96,97,98]
9	Forecasting Emerging Technologies (FET)	7	[99,100,101,102,103]
SDG Themes
10	Infrastructure Development and Innovation (IDI)	5	[104,105,106,107]
11	Sustainable Urbanization and Habitat Development (SUHD)	5	[108,109,110,111,112]
12	Resource Efficiency and Sustainable Practices (RESP)	5	[113,114,115,116,117,118,119]
13	Environmental Conservation and Climate Action (ECCA)	5	[120,121,122,123,124]
14	Social Equity and Economic Development (SEED)	5	[114,125,126,127,128,129,130]
15	Governance and Institutional Support (GIS)	5	[128,131,132,133,134,135,136]

below, this section provides a comprehensive overview of current industry practices and perspectives.

The third section focused on the Sustainable Development Goals (SDGs), emphasizing infrastructure development and innovation, sustainable urbanization, resource efficiency, and environmental conservation. It addressed climate action, social equity, economic growth, governance, and institutional support. The goal was to assess how these SDGs influence sustainable practices within respondents’ roles.

A comprehensive literature review guided the selection of these key topics, ensuring their relevance to sustainability, construction, infrastructure, environmental factors, and governance. The objective was to prioritize these topics, keeping them at the center of the discussion. As a result, this study categorized them into nine core research areas and six Sustainable Development Goal (SDG) themes, forming two distinct yet interconnected datasets.

The survey questionnaire was meticulously structured around these research topics and SDG themes to ensure a well-rounded exploration of sustainability. While the key research areas provided insight into industry best practices, the SDG themes ensured alignment with global sustainability objectives.

The questionnaire was divided into 15 sections, each carefully designed to elicit responses from industry professionals. This approach helped streamline complex information, grouping related variables into coherent clusters for better analysis.

With 98 thoughtfully crafted questions, the questionnaire was strategically designed to explore industry challenges, identify practical solutions, and uncover opportunities, contributing to the advancement of sustainable and innovative practices.

This study utilized exploratory factor analysis (EFA), precisely the principal axis factoring (PAF) method, to analyze the data. Factor analysis is a statistical technique to uncover underlying relationships or structures among a large set of observed variables. Grouping standard variables into smaller sets of factors reduces data complexity and highlights standard dimensions or constructs, offering a clearer understanding of the data’s structure [137,138].

We apply principal axis factoring (PAF), a widely used method in exploratory factor analysis (EFA). PAF is particularly effective in identifying latent variables or factors that explain the shared variance among observed variables. Unlike other methods, it focuses on common variance while excluding unique and error variance, ensuring that the analysis reveals the core dimensions driving the correlations within the dataset [139]. This approach enables us to simplify and interpret complex data, facilitating the identification of meaningful patterns and relationships that are essential to the study’s objectives.

3. Results

3.1. Participant Sociodemographics

This study gathered insights from 79 construction professionals in Chad, examining key demographic aspects such as gender, age, education, job roles, experience, and company size, as shown in

Table 2. Participant Sociodemographics.

Category	Codes and Groups	Percentage (%)
Gender	Male (1)/Female (2)	85/15
Age Group	18–25 (1)/26–33 (2)/34–41 (3)/42–49 (4)/50+ (5)	0/39/52/9/0
Education Level	Architect (1)/Engineer (2)/Construction Manager (3)/Surveyor (4)/Other (5)	4/81/5/-/10
Role in Construction	Architect (1)/Project Manager (2)/Construction Engineer (3)/Sustainability Expert (4)/Other (5)	1/22/56/6/15
Years of Experience	<1 year (1)/1–5 years (2)/6–10 years (3)/11–15 years (4)/15+ years (5)	10/15/53/16/5
Company Size	Small (1)/Medium (2)/Large (3)/Very Large (4)/Other (5)	48/24/9/10/9

below. The findings highlight a significant gender disparity, with men making up 85% of respondents while women account for only 15%. The industry predominantly comprises young professionals, with 39% falling within the 26–33 age bracket and 52% between 34 and 41 years old, whereas none of the participants were over 50. Engineers overwhelmingly dominate the sector, representing 81% of respondents, whereas architects and construction managers are significantly underrepresented, comprising just 4% and 5%, respectively. Regarding job roles, construction engineers comprise the majority at 56%, followed by project managers (22%), sustainability experts (6%), and architects, who make up a relatively small percentage of 1%. Regarding experience levels, most professionals (53%) have worked in the field for 6–10 years, while only 5% have more than 15 years of experience. Additionally, this study reveals that small enterprises are the backbone of the industry, employing nearly half (48%) of those surveyed, underscoring the early-stage industrialization and economic realities of Chad’s construction sector.

3.2. Factor Analysis

Factor analysis is a statistical technique to uncover hidden relationships or structures among observed variables. Identifying correlation patterns reduces data complexity by clustering related variables into smaller sets of factors. These factors represent standard dimensions or constructs, simplifying analysis and interpretation [140].

The questionnaire was structured into three distinct parts. The first part focused on gathering information about the respondents’ sociodemographic characteristics, providing a foundation for understanding their backgrounds. The second part was dedicated to key topics, encompassing nine essential areas central to this study. The third part addressed the Sustainable Development Goals, organized into three main groups, each representing a specific aspect of sustainability.

3.2.1. Key Themes

Following an in-depth literature review, we pinpointed key thematic areas while ensuring that essential Sustainable Development Goals (SDGs) related to sustainability, construction, infrastructure, environmental considerations, and governance remained at the forefront of our study. These critical topics and their corresponding SDGs were the foundation for structuring our survey. Each primary theme was translated into a dedicated section, with carefully formulated statements that allowed respondents to express their perspectives freely.

Ultimately, we established 15 main sections, 9 focused on fundamental research topics and 6 derived from the SDGs, resulting in two separate datasets. In this subsection, we employed principal axis factoring to examine the first dataset, which is structured around the following key topics:

○

Integration of Innovative Technologies (IIT);

○

Sustainability in Construction (SC);

○

Challenges and Solutions (CSs);

○

Correlation with Project Success (CPS);

○

Decision-Making Factors (DMFs);

○

Evaluation of Project Management Software (EPMS);

○

Safety and Risk Management Improvement (SRMI);

○

Environmental and Social Impact Assessment (ESIA);

○

Forecasting Emerging Technologies (FET).

These topics were consolidated into three broader categories to derive meaningful insights while considering the number of items to be processed in IBM SPSS Statistics 30.0. This approach enabled a more structured analysis, allowing a deeper exploration of respondents’ perspectives on the provided statements.

Reliability and Internal Consistency of the Dataset

The Kaiser–Meyer–Olkin (KMO) measure, which ranges from 0 to 1, evaluates the sampling adequacy for factor analysis. A KMO value above 0.5 is considered acceptable by some authors [141], while Pallant (2011) [142] recommends a minimum of 0.6. Kaiser (1974) [143] defines 0.5 as the minimum, categorizing values between 0.6 and 0.69 as mediocre, 0.7 and 0.79 as middling, and 0.8 and 1 as excellent [144]. On the other hand, Bartlett’s Test assesses whether the dataset’s variables are sufficiently correlated for factor analysis. Based on these criteria, our dataset is suitable for factor analysis, as shown in

Table 3. KMO and Bartlett’s Test for the 9 factors.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy.		0.810
Bartlett’s Test of Sphericity	Approx. Chi-Square	5755.040
	df	1891
	Sig.	<0.001

. It reports a KMO value of 0.81, with a statistical significance of p < 0.001.

Key Topic Analysis

To enhance the depth of our analysis, we categorized 62 items representing the statements formulated under the key topics into nine distinct factors corresponding to the main sections of the survey. These factors were then grouped into three broader categories for separate analysis. The first group consists of Decision-Making Factors (DMFs), Forecasting Emerging Technologies (FET), and Environmental and Social Impact Assessment (ESIA). The second group includes Safety and Risk Management Improvement (SRMI), Evaluation of Project Management Software (EPMS), and Integration of Innovative Technologies (IIT). Finally, the third category encompasses Correlation with Project Success (CPS), Challenges and Solutions (CSs), and Sustainability in Construction (SC).

We analyzed each set of factors to generate the following: KMO and Bartlett’s Test, Communalities, Total Variance Explained, scree plot, Factor Matrix, pattern matrix, Structure Matrix, and Factor Correlation. These outputs were essential for assessing the suitability of the data for factor analysis, evaluating variable contributions, and identifying underlying dimensions: the KMO and Bartlett’s Test provided measures of sampling adequacy and overall significance of the correlation matrix. Communalities highlighted shared variance among variables, while the scree plot and variance explained helped determine the optimal number of factors. The matrices and correlations guided interpretation and validation.

Table 4. Total Variance Explained of the first set of factors.

Total Variance Explained
Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	9.261	57.881	57.881	8.952	55.948	55.948	8.110
2	1.315	8.219	66.100	1.025	6.404	62.352	7.260
3	1.203	7.521	73.622	0.891	5.568	67.920	5.161
4	0.681	4.255	77.877
5	0.634	3.964	81.841
6	0.517	3.231	85.072
7	0.435	2.719	87.792
8	0.362	2.265	90.056
9	0.342	2.136	92.192
10	0.288	1.797	93.989
11	0.229	1.432	95.421
12	0.196	1.223	96.644
13	00.172	1.074	97.719
14	0.164	1.028	98.747
15	0.120	0.749	99.495
16	0.081	0.505	100.000

Extraction method: principal axis factoring. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

presents the Total Variance Explained of the first set of factors, showing that Factor 1 accounts for 57.881% of the variance, Factor 2 for 8.219%, and Factor 3 for 7.521%. Combined, these factors explain 73.622% of the total variance in the dataset, which is considered satisfactory and indicates a strong representation of the underlying data structure [145]. Factor 1 emerges as the dominant factor, capturing most of the variance and representing this group’s primary construct. While Factors 2 and 3 explain less variance individually, they still contribute significantly to the overall data structure.

Total Variance Explained

Table 5. Total Variance Explained of the second set of factors.

Total Variance Explained
Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	9.571	56.298	56.298	9.239	54.345	54.345	7.788
2	1.520	8.943	65.240	1.152	6.775	61.121	6.972
3	1.005	5.911	71.151	0.723	4.252	65.372	7.657
4	0.757	4.456	75.607
5	0.697	4.100	79.707
6	0.597	3.514	83.221
7	0.467	2.745	85.965
8	0.403	2.370	88.335
9	0.383	2.256	90.591
10	0.320	1.881	92.472
11	0.283	1.664	94.136
12	0.225	1.321	95.457
13	0.205	1.209	96.665
14	0.177	1.044	97.709
15	0.175	1.032	98.741
16	0.126	0.744	99.485
17	0.088	0.515	100.000

Extraction method: principal axis factoring. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

summarizes the Total Variance Explained of the second set of factors, with Factor 1 accounting for 56.298% of the total variance, Factor 2 accounting for 8.943%, and Factor 3 adding 5.911%. Together, these three factors explain 71.51% of the total variance, indicating that three underlying dimensions effectively represent the dataset.

Table 6. Total Variance Explained of the third set of factors.

Total Variance Explained
Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	8.393	64.562	64.562	8.097	62.287	62.287	7.134
2	0.809	6.220	70.782	0.501	3.850	66.137	6.596
3	0.678	5.216	75.999	0.371	2.852	68.989	6.504
4	0.618	4.753	80.751
5	0.520	4.000	84.751
6	0.392	3.014	87.765
7	0.375	2.887	90.651
8	0.298	2.295	92.947
9	0.231	1.776	94.722
10	0.204	1.567	96.289
11	0.194	1.492	97.781
12	0.163	1.251	99.032
13	0.126	0.968	100.000

Extraction method: principal axis factoring. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

below shows the Total Variance Explained of the third set of factors after the extraction. Factor 1 accounts for 62.287% of the variance, Factor 2 explains an additional 3.850%, and Factor 3 contributes 2.852%. Together, these three factors explain a cumulative 68.989% of the total variance, highlighting the key dimensions represented in the data.

KMO, Pattern Matrix, and Cronbach’s Alpha of the Three First Factors

Table 7. KMO, pattern matrix, and Cronbach’s Alpha of the first set of factors.

Item	KMO	Factor			Cronbach’s Alpha
		1	2	3
DMF4	0.893	0.925			0.939
DMF5		0.873
DMF1		0.755
DMF2		0.744
DMF6		0.736
DMF3		0.699
DMF7		0.670
FET7		0.619
ESIA2			0.938		0.906
ESIA1			0.876
ESIA3			0.662
ESIA7			0.657
ESIA4			0.640
FET1					0.818
FET6				0.956
FET4				0.759

presents the KMO and Cronbach’s Alpha values for Decision-Making Factors (DMFs), Forecasting Emerging Technologies (FET), and Environmental and Social Impact Assessment (ESIA), indicating the adequacy of the sample for factor analysis. The KMO value of 0.893 reflects excellent sampling adequacy, while Cronbach’s Alpha values of 0.939, 0.906, and 0.818 demonstrate strong internal consistency for DMFs, FET, and SIA, respectively.

The pattern matrix, derived from Promax rotation (an oblique method assuming factor correlation), reveals the rotated factor loadings. Factor 1 exhibits strong loadings of DMF variables, including DMF4 (0.925), DMF5 (0.873), DMF1 (0.755), DMF2 (0.744), DMF6 (0.736), DMF3 (0.699), and DMF7 (0.670), highlighting its association with decision-making constructs.

Factor 2 loads strongly on ESIA variables, specifically ESIA2 (0.938), ESIA1 (0.876), ESIA3 (0.662), ESIA7 (0.657), and ESIA4 (0.640), emphasizing its focus on environmental and social impact assessment.

Factor 3 shows high loadings on FET variables, notably FET6 (0.956) and FET4 (0.759), underscoring its alignment with forecasting emerging technologies. These results confirm a well-structured and interpretable factor solution.

The high KMO value (0.912) and Cronbach’s Alpha scores of 0.926, 0878, and 0.847, as shown in

Table 8. KMO, pattern matrix, and Cronbach’s Alpha of the second set of factors.

Item	KMO	Factor			Cronbach’s Alpha
		1	2	3
SRMI5	0.912	0.985			0.926
SRMI2		0.969
SRMI6		0.816
SRMI4		0.549
SRMI3		0.515
EPMS6
IIT5			0.800		0.878
IIT3			0.784
IIT2			0.776
IIT7			0.638
IIT6			0.630
IIT1			0.514
EPMS4				0.910	0.847
EPMS5				0.814

, indicate that the dataset is suitable for factor analysis and has strong internal consistency. Table 8 also presents the pattern matrix, identifying three factors: Factor 1 primarily relates to SRMI, Factor 2 to IIT, and Factor 3 to EPMS. However, EPMS6 and EPMS2 do not exhibit significant loadings on the three factors.

Factor 1 demonstrates strong correlations with SRMI5 (0.947), SRMI2 (0.902), SRMI6 (0.886), SRMI4 (0.728), and SRMI3 (0.726), suggesting it represents the SRMI construct. Factor 2 shows strong correlations with IIT5 (0.810), IIT6 (0.780), IIT3 (0.732), IIT1 (0.701), and IIT2 (0.692), aligning it with the IIT construct.

Some variables exhibit moderate to high correlations with multiple factors. For example, EPMS6 correlates with all three factors (0.771, 0.620, and 0.739) but aligns most strongly with Factor 3. Similarly, EPMS2 correlates with all three factors (0.636, 0.665, and 0.734), primarily aligning with Factor 3. Notably, SRMI3 and SRMI4 show correlations with Factors 1 and 3, suggesting some overlap or shared variance across constructs.

The high KMO value and significant Cronbach’s Alpha scores of 0.920 and 0.887 in

Table 9. KMO, pattern matrix, and Cronbach’s Alpha of the third set of factors.

Item	KMO	Factor			Cronbach’s Alpha
		1	2	3
CPS6	0.931	0.791			0.920
CPS2		0.786
CPS5		0.694
CPS1		0.691
CPS3		0.591
CS3			0.803		0.887
CS4			0.610
CS6
CS5
CS7
SC5				0.865	0.887
SC6				0.716
SC3				0.569

below indicate that the dataset is appropriate for factor analysis and exhibits strong internal consistency.

The pattern matrix below identifies three factors: Factor 1 primarily relates to CPS, Factor 2 to CSs, and Factor 3 to SC. However, CS5, CS6, and CS7 do not show significant loadings on any of the three factors.

Factor 1 demonstrates strong loadings on CPS6 (0.791), CPS2 (0.786), CPS5 (0.694), CPS1 (0.691), and CPS3 (0.591), indicating its alignment with the CPS construct. Factor 2 strongly loads on CS3 (0.803) and CS4 (0.610), suggesting its association with the CS construct. Similarly, Factor 3 has strong loadings on SC5 (0.865), SC6 (0.716), and SC3 (0.569), aligning it with the SC construct.

Despite this structure, variables such as CS5, CS6, and CS7 fail to exhibit significant loadings on any factor, indicating potential issues with their alignment or relevance to the identified constructs. These findings provide a clear basis for understanding the dataset’s factor structure and the relationships among the variables.

3.2.2. Sustainable Development Goals

Building on insights from an extensive literature review, the Sustainable Development Goals were systematically grouped into six key factors, each comprising five items. This classification was based on their connection to critical areas such as sustainability, construction, infrastructure, environmental management, and governance. The resulting factors include Infrastructure Development and Innovation (IDI), Sustainable Urbanization and Habitat Development (SUHD), Resource Efficiency and Sustainable Practices (RESP), Environmental Conservation and Climate Action (ECCA), Social Equity and Economic Development (SEED), and Governance and Institutional Support (GIS).

These six factors are further grouped into two overarching categories, each containing three factors. Group 1 includes Infrastructure Development and Innovation (IDI), Resource Efficiency and Sustainable Practices (RESP), and Environmental Conservation and Climate Action (ECCA). Group 2 encompasses Sustainable Urbanization and Habitat Development (SUHD), Social Equity and Economic Development (SEED), and Governance and Institutional Support (GIS).

This grouping reflects a strategic approach to organizing Sustainable Development Goals, enabling a clearer focus on related domains and their associated objectives. Group 1 emphasizes innovation, resource management, and environmental conservation, highlighting efforts toward sustainable infrastructure, efficient resource use, and climate action. Meanwhile, group 2 centers on urban development, social equity, and governance, addressing broader societal and institutional priorities. This framework offers a structured lens for evaluating progress and interconnections across key dimensions of sustainable development.

Total Variance Explained

Table 10. Total Variance Explained of the fourth set of factors.

Total Variance Explained
Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	8.388	64.523	64.523	8.110	62.383	62.383	7.167
2	1.101	8.467	72.989	0.842	6.476	68.859	6.150
3	0.646	4.971	77.960	0.380	2.923	71.782	6.406
4	0.547	4.207	82.167
5	0.475	3.651	85.818
6	0.447	3.436	89.254
7	0.298	2.290	91.544
8	0.291	2.239	93.783
9	0.253	1.946	95.729
10	0.189	1.456	97.185
11	0.143	1.099	98.284
12	0.128	0.982	99.266
13	0.095	0.734	100.000

Extraction method: principal axis factoring. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

, which shows the total variance explained, reveals that Factor 1 accounts for 64.52% of the variance, while Factor 2 accounts for 8.47%, resulting in a cumulative variance of 72.99%. Factor 3, with an eigenvalue below 1, contributes an additional 4.97%.

After extraction, only the first two factors are retained, as their eigenvalues exceed 1. These factors explain 62.38% and 6.48% of the variance, respectively, accounting for a combined total of 68.86%.

Following rotation, the variance explained is redistributed: Factor 1 contributes 7.17 units, Factor 2 accounts for 6.15 units, and Factor 3 now explains 6.41 units. This adjustment ensures a more balanced distribution of variance among the factors.

According to the eigenvalue criterion, the first three factors are retained, collectively explaining over 70% of the variance (

Table 11. Total Variance Explained of the fifth set of factors.

Total Variance Explained
Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	6.322	63.225	63.225	6.069	60.694	60.694	4.787
2	1.058	10.580	73.805	0.836	8.359	69.053	4.829
3	0.743	7.425	81.230	0.379	3.790	72.843	4.944
4	0.502	5.016	86.246
5	0.379	3.785	90.031
6	0.297	2.966	92.998
7	0.270	2.695	95.693
8	0.181	1.811	97.504
9	0.130	1.296	98.800
10	0.120	1.200	100.000

Extraction method: principal axis factoring. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

). Factors beyond the third are excluded because their eigenvalues fall below 1, indicating minimal contribution to the variance.

KMO, Pattern Matrix, and Cronbach’s Alpha of the Two Last Factors

Table 12. KMO, pattern matrix, and Cronbach’s Alpha of the fourth set of factors.

Item	KMO	Factor			Cronbach’s Alpha
		1	2	3
RESP1	0.901	0.877			0.920
RESP3		0.828
RESP2		0.749
RESP4		0.655
RESP5		0.640
IDI2			0.954		0.887
IDI1			0.863
IDI4			0.552
IDI3
ECCA2				0.785	0.895
ECCA3				0.627
IDI5				0.550
ECCA1

highlights a high KMO value and significant Cronbach’s Alpha, indicating that the dataset is well suited for factor analysis and has strong internal consistency. The pattern matrix, presented in the same table, reveals a clear factor structure, demonstrating that the rotation effectively separated the variables into three distinct and meaningful clusters.

Factor 1 corresponds to Resource Efficiency and Sustainable Practices (RESP), Factor 2 aligns with Infrastructure Development and Innovation (IDI), and Factor 3 represents Environmental Conservation and Climate Action (ECCA). This categorization emphasizes the alignment of variables with their respective constructs, providing a robust framework for interpreting the data.

The KMO value (0.883) and significant Cronbach’s Alpha scores (0.907, 0.888, and 0.866) indicate that the data are highly suitable for factor analysis and exhibit excellent internal consistency. These metrics confirm the reliability of the extracted factors, validating the use of the factor solution for further study and interpretation.

Several items show strong loadings on distinct factors in the pattern matrix (

Table 13. KMO, pattern matrix, and Cronbach’s Alpha of the fifth set of factors.

Item	KMO	Factor			Cronbach’s Alpha
		1	2	3
SUHD3	0.883	0.944			0.907
SUHD5		0.778
SUHD4		0.667
SUHD1		0.653
GIS1			0.937		0.888
GIS2			0.697
SEED3				0.770	0.866
SEED5				0.688
SEED1				0.557
GIS3

). For Factor 1, SUHD3 (0.944), SUHD5 (0.778), and SUHD1 (0.653) load prominently, indicating alignment with Sustainable Urbanization and Habitat Development (SUHD). For Factor 2, GIS1 (0.937) and GIS2 (0.697) exhibit strong loadings, aligning with Governance and Institutional Support (GIS). Lastly, Factor 3 features high loadings for SEED3 (0.770), SEED5 (0.688), and SEED1 (0.557), corresponding to Social Equity and Economic Development (SEED). This structure affirms the robustness and clarity of the factor solution.

4. Discussion

Integrating innovative technologies into construction project management has become a defining feature of the modern construction industry. As the sector continues to evolve, new tools and techniques such as Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology are driving significant transformations. These technologies are reshaping how projects are planned, executed, and maintained, enhancing efficiency, safety, sustainability, and overall project outcomes [146,147]. Adopting these technologies presents promising opportunities and significant challenges in Chad’s construction industry.

4.1. Technological Advancements in Construction Project Management

The rapid advancements in construction technologies are redefining the traditional project workflow [148]. BIM, for example, provides a digital representation of a project’s physical and functional characteristics, allowing for seamless collaboration among project stakeholders. This collaborative platform reduces errors, improves design accuracy, and enhances communication between architects, engineers, and contractors, thereby ensuring that projects are completed on time and within budget [147]. Similarly, AI and IoT enable real-time data collection and predictive analytics, which helps in better decision-making, resource management, and risk mitigation. These technologies allow project managers to make informed decisions, anticipate potential issues, and address them before they escalate, optimizing overall project performance [149].

Moreover, Digital Twins and augmented reality (AR) technologies have introduced new project visualization and coordination possibilities. Digital Twins provide a detailed, real-time view of a construction project by creating digital replicas of physical structures, enabling continuous monitoring and more effective coordination among various teams. Similarly, AR technologies allow project managers to conduct virtual walkthroughs, ensuring that design and construction processes align with the initial vision [150,151,152]. These technological innovations are crucial for ensuring smoother workflows, enhanced productivity, and improved project outcomes.

However, while these technologies promise significant benefits, their integration into Chad’s construction sector faces several obstacles. Limited access to technology, high costs, and a shortage of skilled professionals are among the primary barriers hindering the broader adoption of these tools. Overcoming these challenges will require significant investment in infrastructure, training programs, and government support to ensure that the benefits of innovative technologies can be fully realized.

4.2. Sustainability in Construction and the Role of Innovative Technologies

Sustainability has become a central focus in the construction industry, with a growing emphasis on minimizing environmental impact and fostering social responsibility. Innovations such as eco-friendly materials, 3D printing, and energy-efficient designs play a key role in mitigating the environmental footprint of construction projects. These technologies minimize waste and promote using renewable energy and sustainable building practices [153]. For example, innovative energy systems enable better energy management, while sustainable building techniques ensure that projects contribute to global climate action goals [154].

In Chad, integrating innovative technologies has helped advance sustainability within the construction sector. The Sustainability in Construction (SC) cluster emphasizes the role of technology in waste management, which is crucial for meeting environmental standards. Technologies that enable real-time monitoring of construction activities are improving worker safety and contributing to social sustainability [155]. Furthermore, advancements in technology are enhancing the integration of cost, quality, and risk management, further supporting sustainability goals [156]. However, achieving the full potential of these technologies will require addressing challenges related to access to technology and expertise, which are critical to building a more sustainable construction sector in Chad.

4.3. Challenges and Solutions in Technology Integration

The integration of innovative technologies into Chad’s construction sector presents its challenges. The Challenges and Solutions (CSs) cluster highlights the obstacles to adopting new technologies, including high upfront costs, limited access to technology, and a shortage of skilled professionals. Despite these hurdles, innovative technologies have proven to be instrumental in overcoming many traditional challenges in construction project management. For example, technologies like BIM and AI have significantly improved communication and collaboration among project stakeholders, leading to fewer delays and smoother coordination [2,157,158]. Furthermore, real-time monitoring technologies have enhanced safety on construction sites by allowing project managers to track worker activities and site conditions in real time, reducing the risk of accidents [159].

While these advancements have proven effective in overcoming common project management challenges, addressing the barriers to technology adoption is essential to fully leveraging these tools [160]. Ensuring widespread access to technology, providing adequate workforce training, and reducing the costs associated with these technologies will be crucial for maximizing their impact on Chad’s construction industry.

4.4. The Correlation Between Technology and Project Success

There is a strong correlation between adopting innovative technologies and the success of construction projects in Chad’s construction industry. The Correlation with Project Success (CPS) cluster emphasizes how advanced technologies contribute to improved project outcomes. The integration of tools like BIM, AI, and IoT has been shown to increase productivity, help projects stay within budget, and ensure timely completion [2]. These technologies reduce the risks of miscommunication, errors, and disruptions, enhancing project efficiency. By facilitating better decision-making and offering real-time insights, these technologies also contribute to improved quality and performance [2].

Data-driven approaches, powered by AI and IoT, enable project managers to make more informed decisions confidently. Real-time dashboards and scenario modeling tools provide actionable insights, helping to reduce uncertainties and enhance strategic planning [161,162]. The ability to monitor project progress in real time and the capacity to make data-driven adjustments significantly improve the likelihood of project success.

4.5. Project Management Software and Its Role in Technological Integration

As the construction industry in Chad continues to modernize, the importance of project management software has become increasingly evident. Platforms such as Procore, Primavera, and PlanGrid provide robust solutions tailored to the construction industry’s specific needs, enabling more efficient project tracking, resource allocation, and communication [163]. The Evaluation of Project Management Software (EPMS) cluster emphasizes the importance of selecting customizable and scalable software, ensuring it can be tailored to the unique needs of each project.

For Chad’s construction industry, overcoming barriers to access and expertise is critical to fully leveraging these software tools. Customization enables project managers to tailor the software to their specific requirements, while scalability ensures it can grow and evolve in line with the project. Additionally, the focus on optimizing resource allocation is helping to improve productivity and streamline workflows [164].

4.6. Safety and Risk Management Improvements

Safety is a critical concern in the construction industry, and technological advancements are playing a pivotal role in improving safety standards [155]. Wearable devices, drones, and AI-driven hazard assessments have made significant strides in reducing workplace accidents and ensuring compliance with safety regulations [163]. The Safety and Risk Management Improvement (SRMI) cluster highlights how predictive analytics, real-time monitoring, and advanced tools improve construction site safety protocols. Drones, IoT sensors, and AI technologies can monitor worker activity, detect potential hazards, and ensure timely interventions, significantly reducing the risk of accidents [165].

Moreover, virtual reality (VR) and augmented reality (AR) enhance safety training, enabling workers better to understand potential risks in a controlled, immersive environment. These technologies enable more proactive safety management and contribute to creating safer work environments [166].

4.7. Environmental and Social Impact Assessments

Technological advancements also enhance the accuracy and efficiency of Environmental and Social Impact Assessments (ESIAs). GISs, remote sensing, and community mapping technologies enable project managers to assess construction projects’ environmental and social implications in greater detail. These tools help to streamline workflows, ensure projects align with sustainability goals, and address the needs of local communities [167,168]. In Chad, integrating these technologies into ESIA processes enhances the ability to monitor and manage environmental factors, thereby supporting responsible construction practices.

While these technologies offer significant benefits, challenges related to access to technology, expertise, and financial resources remain. Overcoming these challenges is essential for ensuring that Chad’s construction industry can fully capitalize on the benefits of these innovations and contribute to global sustainability efforts [169].

4.8. The Future of Construction: Emerging Technologies

The construction industry is poised for further transformation with the advent of emerging technologies, including autonomous construction equipment, blockchain for contract management, and robotics. These technologies promise to enhance efficiency, reduce costs, and minimize environmental impact, further reshaping the industry [170]. The Forecasting Emerging Technologies (FET) cluster emphasizes adopting these technologies to drive continued innovation in Chad’s construction sector.

By collaborating with external partners, industry stakeholders in Chad can bridge technological gaps, promote knowledge sharing, and accelerate the adoption of these cutting-edge tools. However, challenges such as limited expertise, financial constraints, and resistance to change may slow the process. Despite these obstacles, the growing interest in these emerging technologies signals a forward-thinking approach that could further improve the efficiency and sustainability of construction projects in Chad.

4.9. Smart Infrastructure and Sustainable Urbanization

Integrating IoT and AI also contributes to developing smart infrastructure, which enhances connectivity, energy efficiency, and overall functionality. Furthermore, adopting prefabrication and modular construction methods accelerates project timelines and reduces resource waste, particularly in urban areas [171]. The Infrastructure Development and Innovation (IDI) cluster reflects Chad’s commitment to adopting cutting-edge technologies that improve infrastructure project efficiency while promoting sustainability and resilience.

Similarly, the Sustainable Urbanization and Habitat Development (SUHD) cluster emphasizes the importance of sustainable urban planning and habitat development in Chad. This includes preserving green spaces, promoting inclusive growth, and adopting eco-friendly building standards. Technologies like GISs and BIM play a crucial role in advancing these objectives, enabling efficient planning, enhancing sustainability, and fostering community participation in urban development projects [172].

Integrating innovative technologies in Chad’s construction sector presents opportunities and challenges. While these technologies can potentially improve efficiency, safety, sustainability, and project outcomes, the industry must overcome barriers such as limited access to technology, high costs, and a lack of technical expertise. Government support, strategic collaborations, and ongoing education are essential to overcoming these challenges and ensuring that Chad’s construction sector can fully embrace the benefits of technological innovation. With the right investments and a commitment to modernization, Chad’s construction industry can lead the way in adopting cutting-edge technologies that support sustainable development and economic growth.

5. Conclusions

The integration of innovative technologies into construction project management has fundamentally reshaped the industry, driving greater efficiency, sustainability, and safety. Cutting-edge solutions like Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology have revolutionized the way projects are conceived, executed, and maintained, streamlining processes and addressing traditional inefficiencies. For Chad, embracing these advancements represents a significant step toward modernizing its construction sector while aligning with global sustainability objectives. However, unlocking the full benefits of these technologies requires overcoming critical challenges.

One of the primary hurdles is limited access to advanced tools and the technical expertise needed for successful implementation. Many of these technologies come with high costs and require specialized training, both of which remain scarce in Chad. Tackling this issue will require strategic investments in infrastructure, workforce training, and education. Government initiatives will play a crucial role in developing policies and funding programs that promote adopting technology and skill development. Fostering international collaborations can facilitate knowledge transfer, helping Chad bridge the gap and fully leverage these innovations.

Future research should prioritize exploring ways to make these technologies more affordable and practical for widespread adoption. Identifying cost-effective alternatives, such as open-source software or modular digital tools, could significantly reduce financial barriers, particularly for smaller firms. Furthermore, developing tailored training programs would help cultivate a skilled workforce, ensuring long-term innovation and industry growth.

Another promising avenue for research is the link between technology and sustainability. Investigating how AI and IoT can minimize resource waste, optimize energy efficiency, and promote eco-friendly construction could offer invaluable insights into building a more sustainable industry. Studies could also explore how advancements in prefabrication, modular construction, and the integration of renewable energy impact urban development and environmental conservation over time.

Furthermore, emerging technologies like autonomous construction equipment, robotics, and blockchain-based contract management hold significant potential to revolutionize the industry. Assessing their feasibility and scalability within Chad’s specific context could highlight both opportunities and potential obstacles. Moreover, integrating IoT-powered energy management systems and innovative urban planning solutions could help Chad address urbanization challenges while fostering sustainable and inclusive development.