Digital Evolution in Nigerian Heavy-Engineering Projects: A Comprehensive Analysis of Technology Adoption for Competitive Edge

Abstract

The fourth industrial revolution has introduced a range of digital technologies (DTs) that possess the potential to significantly enhance the operations and competitiveness of heavy-construction firms. Grounded in the Technology–Organization–Environment (TOE) Framework, the Resource-Based View (RBV) and the Diffusion of Innovation Theory (DOI), this study investigates the relationship between the adoption of digital technologies and the competitive edge (CE) of heavy-engineering firms. Specifically, this research seeks to assess how the adoption of DTs impacts four critical competitive-edge metrics: efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4). A quantitative research approach was employed, using a structured questionnaire distributed to construction professionals in Lagos State, Nigeria. The principal results of the study revealed that firms adopting artificial intelligence (AI), cloud-based technology and the Internet of Things (IoT) exhibited significantly higher competitive-edge metrics compared to their counterparts. Notably, AI and cloud-based technology were found to have a particularly strong association with improved resource management, real-time monitoring, and decision-making processes. A major contribution of this research is the development of a DT-adoption model which can serve as a benchmarking tool for firms to assess their current adoption levels and identify areas for improvement. This model can also guide policymakers and regulators in developing strategies to encourage the integration of digital technologies within the heavy-construction industry. The originality of this study lies in its holistic approach, examining a broad spectrum of digital technologies and their collective impact on enhancing the competitive edge of construction firms.

1. Introduction

Digital technologies have ushered in a new era of unprecedented levels of automation, connectivity and data exchange, prompting sectors to adapt and embrace this wave of innovation to stay relevant, or risk falling behind in the ever-evolving digital race [1,2,3]. Like several sectors across the globe, digital technologies have become increasingly prevalent in various facets of the architectural, engineering, construction and operation (AECO) sector. They are being utilized not only in building construction and architectural design but also in civil engineering projects and heavy-engineering projects; these encompass large-scale infrastructure projects such as bridges, roads, dams, power plants and other complex structures or systems [4,5]. Several digital technologies have made inroads into heavy-engineering projects, including artificial intelligence (AI), building information modeling (BIM), cloud-based project management platforms, advanced analytics and machine learning, 3D scanning and drone technology, virtual reality (VR) and augmented reality (AR), computer-aided design (CAD), Internet of Things (IoT) and sensor technology, digital twins, advanced communication tools, robotics and autonomous systems and blockchain technology, among several others [6,7].

Despite the adoption of these technologies, there is a clear research gap in understanding which specific digital technologies provide a distinct competitive edge (CE) in the heavy-construction industry, particularly in relation to measurable metrics like efficient resource management, real-time monitoring and decision-making. While some studies have explored how digital technologies impact project performance, few have assessed their direct contribution to enhancing a firm’s competitive position in the marketplace [8]. This gap is particularly evident in regions such as Lagos State, Nigeria, where construction firms are increasingly adopting digital technologies but lack insights on which technologies provide the most substantial competitive advantage over their rivals. A challenge also remains in determining which DTs provide a CE to adopters in terms of achieving key metrics such as efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and enhanced collaboration and communication (CE4), compared to their closest competitors. A heavy-construction firm’s nearest competitor is a rival company operating in the same industry, offering similar services or products and competing for the same contracts or projects within the construction sector [8]. According to [9], heavy-construction firms prioritize investing in various digital technologies to streamline their operations, improve project outcomes and maintain a competitive edge. However, many firms are uncertain about the specific technologies that will provide the greatest strategic benefits. By evaluating the impact of various DTs, businesses can make informed decisions about which technologies will most effectively enhance their competitive edge. This is crucial for firms looking to optimize their resources and project outcomes in an increasingly competitive market.

Hence, the primary research inquiry guiding this investigation is as follows: what are the specific digital technologies (DTs) that significantly enhance the competitive edge (CE) of heavy-construction firms in Lagos State, Nigeria? This study aims to examine the extent of implementation of each DT, analyze the correlation between DT adoption levels and the competitive edge of heavy-engineering firms and provide recommendations regarding the adoption of specific DTs by heavy-construction firms to attain a competitive edge. Findings from this study will equip construction firms with data-driven insights on which digital technologies offer the most cost-effective path to achieving a competitive edge. Moreover, it will address the practical challenge of helping construction firms prioritize their digital investments based on technologies that provide measurable improvements in efficiency and competitiveness. This knowledge can guide strategic investments in technology and ensure that heavy-construction firms focus on solutions with the greatest potential for boosting efficiency, project outcomes and overall competitiveness.

2. Literature Review

2.1. Digital Technologies for Heavy-Construction Projects

According to [8,10], the heavy-construction industry encompasses the planning, design and execution of large-scale infrastructure projects, including, but not limited to, bridges, highways, dams, tunnels, airports, railways, power plants, canals, industrial facilities and many more. Unlike residential or commercial construction, heavy-construction projects typically involve extensive engineering, specialized equipment and significant resources due to their scale and complexity [11]. These large-scale projects also require long durations and intricate project timelines. Unlike smaller-scale construction activities, heavy-construction projects often span several years from inception to completion and they entail detailed planning, design iterations, environmental assessments, regulatory approvals and construction phases, each of which contributes to prolonged timelines [12]. Moreover, unforeseen challenges such as adverse weather conditions, geological complexities and logistical constraints can further extend project durations.

Existing studies on the impact of digital technologies in the heavy-construction industry are evident in the literature, and they have focused on various aspects. These include types of available technological solutions, positive outcomes of digital technologies, levels of awareness and adoption, hindrances to adoption, future trends of adoption, the role of digital technologies in promoting environmental sustainability and strategies to boost adoption. The study described in [13] classified digital technologies in terms of their functionality in heavy-construction projects into design and planning technologies, project management and collaboration technologies, site surveying and mapping technologies, construction automation and robotics, sensor technologies, environmental monitoring and sustainability technologies, safety technologies, data analytics and decision support systems. Some of the digital technologies that are commonly used in heavy-construction firms include building information modeling (BIM), unmanned aerial vehicles (UAVs), geographic information systems (GIS), Internet of Things (IoT), computer-aided design (CAD) software, virtual reality (VR) and augmented reality (AR) systems, robotics, artificial intelligence (AI) algorithms, cloud computing, 3D printing, Big Data, global positioning systems (GPS), radio frequency identification (RFID) and several others [2,3,6,14,15,16].

The gains of digital technologies in heavy-construction firms have been well discussed in existing studies, as these technological advancements lead to improved project efficiency, enhanced collaboration among stakeholders, reduced costs and increased safety levels. For instance, the implementation of BIM leads to better coordination among design, engineering and construction teams, resulting in fewer clashes and conflicts during the construction phase [15]. Geographic information systems (GIS) facilitate better spatial analysis and decision-making, optimizing route alignments and resource allocation [3]. Additionally, the integration of the Internet of Things (IoT) devices provides real-time data on equipment performance and environmental conditions, enabling proactive maintenance and improved safety protocols [12]. Moreover, the AI algorithms enhance predictive analytics, enabling proactive risk management and resource optimization throughout the project lifecycle. Despite the apparent benefits of digital technologies in heavy-construction firms, their adoption has continued to be hindered in both developed [17,18,19] and developing countries [11,16,20]. The authors acknowledge that numerous studies on these barriers exist; some of the leading hindrances are lack of stakeholder awareness, high initial implementation costs, resistance to change in organizational cultures, fragmented regulatory frameworks, limited interoperability between digital tools, concerns about data privacy and security, and shortage of skilled personnel, among several others [12,18,21].

2.2. Knowledge Gap

As previously stated, numerous studies have explored the benefits of digital technologies and how they are transforming the activities, operations and processes of the heavy-construction industry. However, many of these studies have focused solely on existing technologies, without adequately addressing multiple performance outcomes or considering the impact of the new technologies on firms as to their gaining a competitive edge against their nearest competitors. This study not only identifies some of these digital technologies but also investigates their influence on key competitive metrics such as efficient resource management, real-time monitoring and control, data-driven decision-making and improved collaboration and communication compared to one’s nearest competitor.

2.3. Relevant Theories on Adopting Innovative Technologies

Considering the relatively new nature of several of these technologies in the heavy-construction sector, they can be classified as innovations. The three theories that provide valuable insights into the adoption of such innovative technologies are the Technology-Organization-Environment (TOE) Framework, the Resource-Based View (RBV) and the Diffusion of Innovation Theory (DOI).

The TOE framework suggests that technology adoption hinges on understanding the technology’s impact, organizational readiness and the external environment [22]. According to this framework, the characteristics of the technology include its complexity, compatibility with existing systems and relative advantage over alternative solutions. The organizational context encompasses factors such as the firm’s size, structure, culture and capabilities, which may affect its ability to adopt and utilize the technology effectively. Finally, the external environment includes industry competition, regulatory requirements, market dynamics and technological trends, all of which can shape the opportunities and challenges associated with technology adoption [22].

The RBV focuses on how a firm’s unique and valuable resources create a competitive advantage. It suggests that firms with valuable, rare, inimitable and non-substitutable resources are better-positioned to achieve sustainable competitive advantage [23]. With this study focused on technology adoption in the heavy-construction sector, the RBV lens allows us to analyze how integrating innovative technologies can create valuable and inimitable resources for firms. This could include proprietary software systems, specialized equipment, or unique organizational capabilities developed through the adoption and utilization of digital technologies. By leveraging digital tools to develop these unique capabilities, heavy-construction firms can differentiate themselves from competitors and gain a sustainable competitive advantage. The RBV perspective thus emphasizes the strategic importance of technology adoption in enhancing firms’ resource base and positioning them for long-term success in the industry.

Finally, the DOI focuses on how innovations spread and are adopted within a social system over time [24]. According to DOI, the adoption of innovative technologies is influenced by five key characteristics: relative advantage, compatibility, complexity, trialability and observability [24]. With regard to technology adoption in the heavy-construction sector, DOI provides insights into how the characteristics and perceived benefits of digital technologies influence their adoption rate. It helps explain why some technologies are quickly adopted by certain firms or industries while others face resistance or slower adoption rates.

The rationale for selecting the TOE, RBV and DOI frameworks stems from their complementary ability to explain the multidimensional nature of DT adoption in the heavy-construction sector. The TOE framework provides a holistic view by considering the technology, organizational context and external environment, which are all crucial for understanding the adoption process in this dynamic sector. The RBV offers a strategic perspective, focusing on how digital technologies can create unique, inimitable resources that provide a competitive advantage, aligning with the study’s goal of exploring how technology adoption enhances a firm’s competitive edge. Also, the DOI theory is instrumental in understanding the adoption process over time, explaining the factors that influence the rate of adoption and the spread of innovations. Together, these frameworks align with the study’s objectives of analyzing the impact of DT adoption on competitive-edge metrics and providing insights into the drivers and barriers to adoption in the heavy-construction industry.

2.4. Theoretical Framework Underpinning This Study

The theoretical framework guiding this study—aimed at addressing the existing knowledge gap—is illustrated in Figure 1. Drawing on the research in [2], a set of 20 digital technologies is identified from the existing literature. Although numerous digital technologies exist, these 20 were chosen as a starting point for this study. This selection allows for a focused analysis while acknowledging the potential for further exploration of additional technologies in future research. When operationalizing dependent variables, DOI theory emphasizes the influence of both an innovation’s perceived benefits compared to existing solutions (relative advantage) and the ease of observing its tangible outcomes. For this study, the dependent variables include the firm’s efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4), each considered in comparison to the nearest competitor. The relationship between the degrees of implementation for various digital technologies (DT variables) and the competitive edge (CE1 to CE4) experienced by construction firms is then determined. The four dependent variables were selected because they capture key dimensions of competitive advantage in the industry. Efficient resource management and real-time monitoring are critical for optimizing project delivery, while data-driven decision-making enables firms to leverage technology for more informed strategic choices. Improved collaboration and communication reflect the increasing importance of integration and coordination across project teams [11,16,20]. Together, these four dependent variables cover critical aspects of a firm’s competitive edge, ensuring that the study provides a well-rounded understanding of how digital technologies influence competitiveness in the heavy-construction sector.

Each DT may exhibit significant correlations with, one, two, three, or all four CE metrics, or with none of them. The decision regarding the allocation of resources for adopting each DT is determined by its correlation with competitive-edge (CE) metrics. If a DT is significantly correlated with all four metrics, it warrants intensive adoption efforts. Conversely, if it lacks a significant correlation with any metric, adoption efforts should be minimal as the technology may not provide immediate gains. For DTs significantly correlated with one metric, adoption strategies should proceed only after extensive evaluation. For those correlated with two metrics, strategies should proceed cautiously and for those correlated with three metrics, strategies should advance steadily. This research is guided by both the TOE and RBV frameworks to recommend strategies for enhancing the adoption of DT that are currently not optimized. The TOE framework’s emphasis on technological factors (the capabilities of the digital technology itself), organizational factors (the firm’s culture, resources and leadership) and environmental factors (industry regulations and competition) when influencing technology adoption makes it a suitable option for this study. The RBV framework is valuable because it suggests that successful technology adoption can lead to the creation of valuable and inimitable resources for a firm. These resources can then be leveraged to gain a competitive edge.

3. Research Method

This study is part of a broader investigation into the integration of digital technology tools within heavy-engineering projects in Lagos State, Nigeria. It evaluates aspects such as the awareness of, adoption of, and benefits of digital technologies in the sector, as well as the barriers to and drivers behind their integration. Specifically, this research focuses on how the adoption of digital technologies influences the competitive edge (CE) of heavy-engineering firms. A quantitative research approach was employed to systematically collect and analyze numerical data, providing a measurable framework for investigation. This method enables the acquisition of a large dataset, enhancing statistical power and facilitating robust conclusions. Figure 2 presents a summary of the research process and corresponding deliverables.

Data collection was accomplished through the administration of a questionnaire which served as a structured tool to gather essential information for the research study. The questionnaire was divided into three segments. The first segment obtained demographic information from respondents, while the second section featured a list of 20 prevalent digital technologies (DTs). To assess varying levels of familiarity with these technologies, participants were asked to evaluate their engagement using the following classifications: A = indicating no prior exposure; B = reflecting awareness but a lack of interest in further pursuit; C = signifying awareness without current usage but a willingness to explore; D = representing active exploration without current implementation; E = denoting modest utilization; F = expressing regular and moderate engagement; G = showcasing extensive application; H = highlighting thorough and active utilization; and I = characterizing extensive usage coupled with the active development of customized applications for the firm. These nine initial response options were transformed into a 5-point scale during data cleaning. This process (coded A and B as 1, C and D as 2, etc.) enabled the creation of an adoption score for each technology. The rationale for this adjustment was to enhance clarity and ease of interpretation by grouping closely related categories into broader classifications. This step minimized potential variability caused by fine distinctions in responses that might not significantly impact analytical outcomes. Additionally, the simplified scale allowed for the application of more robust statistical techniques while maintaining meaningful distinctions between levels of digital technology adoption.

The survey’s third section further explored the impact of these technologies by prompting respondents to assess their organization’s competitive edge (CE) across the four metrics: efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3), and improved collaboration and communication (CE4). To gauge respondents’ perception of their firm’s competitive edge, a 5-point Likert scale was employed. This scale measured the perceived gap between the firm and its nearest competitor. A score of 1 indicated a significant disadvantage, while a score of 5 reflected a clear competitive advantage. The middle point (3) signaled that the two firms were perceived to be at a similar level.

To ensure the clarity and effectiveness of the survey instrument before wider distribution, a pilot test was conducted using convenience sampling. Fifteen participants were selected, comprising twelve practitioners with a minimum of 20 years of experience, including specialists in sustainable building materials and digital project management. Additionally, three research advisors with expertise in building information modeling (BIM) adoption participated. Feedback from the participants highlighted several areas for refinement. Specifically, participants noted that some of the descriptors were perceived as ambiguous, leading to inconsistent interpretations. For instance, terms related to the level of DT adoption and specific operational impacts were not universally understood across different professional backgrounds. In response to this feedback, revisions were made to clarify the language, ensuring more precise definitions and removing any jargon that could potentially confuse respondents. Also, the order of certain questions was adjusted to improve the logical flow of the survey and reduce respondent fatigue. The pilot test also helped identify and address potential issues related to specific survey questions, particularly those concerning the clarity of the nine descriptors (A to I). The pilot study thus contributed to the reliability and validity of the research instrument by ensuring that questions were clear, consistently interpreted, and relevant to the research objectives [25]. This process helped minimize ambiguity, enhance the consistency of responses, and improve the overall effectiveness of the data collection tool, thereby strengthening both the accuracy and credibility of the findings. The completed questionnaires were distributed through a combination of Google Forms, chosen for its user-friendliness and the secure survey platform it offers to ensure data privacy.

The study focused on professionals within the heavy-engineering industry in Lagos State, Nigeria; the population comprised individuals who were professionally registered with their respective bodies. Analysis of the available annual reports revealed a total population of 6436 members, including 1870 architects, 842 builders, 2585 engineers and 1139 quantity surveyors (Nigerian Institute of Architects (NIA)/Architects Registration Council of Nigeria (ARCON), 2023; Council of Registered Builders of Nigeria (CORBON)/Nigerian Institute of Building (NIOB), 2023; Council for the Regulation of Engineering in Nigeria (COREN)/Nigerian Society of Engineers (NSE), 2023; Quantity Surveyors Registration Board of Nigeria (QSRBN)/Nigerian Institute of Quantity Surveyors (NIQS), 2023). Applying the Yamane equation [26] to achieve a 5% precision level (e), a sample size of 376 respondents was calculated based on the population of 6436 construction professionals. The use of the Yamane equation was deemed appropriate due to its suitability for determining a representative sample size from a large population, ensuring statistical accuracy and reliability in the subsequent analysis of data. Equation (1) presents the Yamane sample-size formula.

$$n={\displaystyle \frac{N}{1+N{\left(e\right)}^2}}$$

(1)

where n = sample size; N = population; and e = error margin (5%).

To secure a representative sample from a diverse range of firms, a dual-method strategy was implemented. Firstly, the approach involved random sampling, wherein emails were sent to firms selected randomly from the sampling frame, inviting them to participate in the research. Secondly, a purposive sampling technique was employed, identifying potential respondents on LinkedIn through the use of specific keywords. These construction professionals were then screened for eligibility before receiving invitations to take part in the study. This dual-method strategy was designed to ensure the acquisition of a representative sample that was relevant to the study. While purposive sampling was used to ensure participant expertise, this approach may limit external validity, reducing generalizability to other contexts. This is further addressed in the limitation section of the manuscript. Electronically, 229 individuals responded to the survey out of the 376 questionnaires administered, yielding a response rate of around 61%. This response rate was considered satisfactory, aligning with findings from similar studies, such as the one conducted by [11].

Methodology for Analyzing Data

Data analysis was conducted using the Statistical Package for Social Sciences (SPSS) software version 26. The interpretation of results primarily focused on statistical significance. A significance threshold of 0.05 was applied to the p-value, where values below 0.01 were considered statistically significant and values below 0.005 were considered highly statistically significant [27]. Since the Shapiro–Wilk test indicated that the data was not normally distributed (p < 0.05), non-parametric tests like the Mann–Whitney U test were used for analysis. Cronbach’s alpha provided high reliability, with alpha values of 0.918 for Section 2 and 0.936 for Section 3 (both exceeding the recommended threshold of 0.7) [28]. To assess whether the median adoption level for each digital technology differed significantly from 3 (the midpoint) on the 5-point scale, a non-parametric Wilcoxon signed-ranks test was employed. In cases of technologies with a mean score above 3 (indicating adoption above the average level) and a p-value below 0.05, it was concluded that their adoption was statistically significant. Conversely, technologies with a mean below 3 (suggesting adoption below the average level) and a p-value below 0.05 were deemed to have either no or minimal adoption. Also, technologies with a p-value greater than or equal to 0.05 were considered to be adopted to a moderate extent.

To investigate whether different respondent groups exhibited significant variations in digital-technology-adoption rates, a Mann–Whitney U test was employed [29]. The null hypothesis (H₀) posits that the two groups have equivalent adoption rates, while the alternative hypothesis (H₁) asserts the opposite, suggesting they differ in adoption rates. A p-value lower than 0.05 indicates a statistically significant difference in adoption rates between the two groups. To explore the relationship between the level of digital technology (DT) usage and various competitive-edge (CE) variables (CE1, CE2, CE3 and CE4), Spearman’s rank correlation coefficient was employed. A statistically significant correlation between a specific DT variable and a CE variable was concluded when the resulting p-value was lower than 0.05. To explore how the level of adoption of various digital technologies (independent variables) influences each outcome variable (CE1 to CE4), a multiple linear regression (MLR) analysis was conducted.

The model’s ability to predict the actual values of the CE variables was assessed through “goodness of fit” measures, namely the coefficient of determination (R²) and adjusted R² [30]. R² reflects the proportion of the variance in the CE variable (dependent variable) attributable to the DT variables (independent variables) within the model. R² values range from 0 to 1, with higher values indicating a better fit between the model and the observed data. However, R² can be inflated with the inclusion of additional independent variables, regardless of their actual predictive power. Therefore, adjusted R² is often considered a more reliable indicator of model fit, as it factors in the number of independent variables used in the analysis [30].

4. Results

4.1. Background Information of Respondents

Table 1. Characteristics of respondents who participated in this study.

Description	Frequency	Percentage
Professional Rank
Executive level	61	26.63
Managerial level	70	30.57
Technical specialist	98	42.80
Total	229	100
Years of industry experience
1 year to 5 years	66	28.80
6 years to 10 years	89	38.86
11 years to 20 years	57	24.89
Over 25 years	17	7.42
Total	229	100
Involvement in technology integration
Assess, analyze and/or suggest technologies for consideration	36	15.72
Make determinations regarding the procurement and/or integration of technologies	78	34.06
Engage in the application and assimilation of technologies	91	39.74
Additional roles or responsibilities	24	10.48
Total	229	100
Occupational position
Architects	45	19.65
Builders	52	22.71
Engineers (mechanical, electrical, etc)	66	28.81
Quantity surveyors	51	22.26
Others	15	6.57
Total	229	100

Source: Author’s own work.

and

Table 2. Attributes of firms whose representatives participated in this study.

Description	Frequency	Percentage
Industry segmentation
Contractor	135	58.96
Consultant	94	41.04
Total	229	100
Industry of operation
Civil engineering and infrastructure	64	27.94
Transportation and highways	32	13.97
Industrial plant construction	34	14.85
Water and environmental engineering	45	19.65
Urban design	51	22.26
Others	3	1.33
Total	229	100
Years of operation
1 year to 5 years	35	15.28
6 years to 10 years	56	24.45
11 years to 20 years	72	31.41
Over 25 years	66	28.86
Total	229	100

Source: Author’s own work.

present a summary of the respondents and their respective firms. Table 1 reveals diverse participant characteristics, with 26.63% in executive roles, 30.57% in managerial positions and 42.80% as technical specialists. In terms of experience, 38.86% have 6 to 10 years, 24.89% have 11 to 20 years and 7.42% have over 25 years in the industry. Regarding technology integration, 39.74% are actively engaged in application and assimilation, 34.06% make determinations on procurement and 15.72% assess and suggest technologies. In terms of industry segmentation, the majority of participating firms (58.96%) identify as contractors, while consultants–owners constitute 41.04%. Concerning the years of operation, a notable portion (31.41%) have been established for 11 to 20 years, demonstrating a blend of experienced and relatively newer firms in the study, as shown in Table 2.

4.2. Level of Implementation of Digital Technologies

The first objective of this study was to evaluate the level of implementation for various digital technologies used in heavy-construction firms. The Wilcoxon signed-rank test was employed to assess these implementations. The results revealed that three digital technologies were adopted to a statistically significantly higher degree. These technologies included artificial intelligence (AI) (DT1), cloud-based technology (DT5) and the Internet of Things (IoT) (DT9). For these technologies, the level of implementation was greater than 3 and the significance level (p-value) was less than 0.05. Other technologies, such as DT2, DT4, DT6, DT7 and DT12, are used to a moderate level. In contrast, DT3, DT8, DT10, DT11, DT13 and DT14—DT20 are not adopted or barely adopted, due to mean values less than 3 and a significance level (p-value) below 0.05.

4.3. Relationship Between Adoption Level and Competitive Edge

To further explore the link between digital technology adoption (DT variables) and a construction firm’s competitive edge (CE1 to CE4), Spearman’s rank correlation analysis was utilized (refer to

Table 3. Implementation of digital technologies and correlations with outcomes.

Code	Digital Technologies	Mean	Wilcoxon b	Efficient Resource Management (CE1)	Real-Time Monitoring and Control (CE2)	Data-Driven Decision-Making (CE3)	Improved Collaboration and Communication (CE4)
DT1	Artificial intelligence (AI)	4.65	8.321 **	—	0.451 **	0.412 **	0.412 **
			0.000	—	0.362	0.212	0.212
				—	—	—	—
				—	—	—	—
				—	—	—	—
DT2	Radio frequency identification (RFID)	3.11	—0.421	—	0.265 *	0.313 *	—
			0.362	—	0.025	0.025	—
				—	0.367 *	0.267 *	—
				—	0.033	0.033	—
				—	—	—	—
				—	—	—	—
DT3	Building information modeling (BIM)	2.03	6.873 **	—	—	0.216 *	0.231 **
			0.000	—	—	0.001	0.001
				—	—	0.254 *	0.245 **
				—	—	0.004	0.013
				—	—	—	—
DT4	Robotic technology	3.01	3.413 **	0.322 **	—	0.255 **	—
			0.000	0.023	—	0.017	—
				0.322 **	—	0.340 **	—
				0.002	—	0.003	—
				—	—	—	—
DT5	Cloud-based technology	3.94	3.453 **	0.167 **	0.356 **	0.316 **	—
			0.000	0.004	0.001	0.292	—
				0.412 **	0.302 **	—	—
				0.001	0.021	—	—
				0.421 **	—	—	—
				0.009	—	—	—
				—	—	—	—
DT6	Design for manufacturing and assembly (DfMA)	3.05	−4.923 **	—	—	0.234 *	0.225 *
			0.256	—	—	0.017	0.026
				—	—	0.318 *	—
				—	—	0.022
				—	—	—	—
DT7	Augmented/virtual/mixed reality (AR/VR/MR)	3.10	−2.654 **	—	—	—	—
			0.367	—	—	—	—
				—	—	—	—
				—	—	—	—
				—	—	—	—
DT8	Machine learning	2.15	0.982	0.303 **	—	0.266 **	—
			0.001	0.003	—	0.031	—
				0.287 **	—	—	—
				0.022	—	—	—
				—	—	—	—
DT9	Internet of Things (IoT)	3.09	6.112 **	—	0.205 **	—	—
			0.000	—	0.013	—	—
				—	—	—	—
				—	—	—	—
				—	—	—	—
DT10	Big Data	2.42	−5.344 **	0.223 **	—	0.276 **	—
			0.337	0.005	—	0.009	—
				0.111 **	—	0.221 **	—
				0.082	—	0.037	—
				—	—	—	—
DT11	Computer-aided design (CAD)	2.41	−0.335	—	0.266 **	—	0.333 **
			0.000	—	0.031	—	0.001
				—	0.222 **	—	0.316 **
				—	0.008	—	0.001
				—	—	—	—
				—	—	—	—
				—	—	—	—
DT12	Geographic information system (GIS)	3.11	−2.097	—	—	—	—
			0.412	—	—	—	—
				—	—	—	—
				—	—	—	—
				—	—	—	—
DT13	3D printing or additive manufacturing (AM)	2.92	−1.632	—	—	—	—
			0.001	—	—	—	—
				—	—	—	—
				—	—	—	—
				—	—	—	—
DT14	Computer-aided manufacturing	2.01	0.224	0.116 **	—	—	0.222 **
			0.000	0.001	—	—	0.028
				—	—	—	0.102 **
				—	—	—	0.011
				—	—	—	—
DT15	Sensor-based technology	2.32	−3.453 **	0.294 **	0.176 **	—	—
			0.000	0.026	0.025	—	—
				0.267 **	—	—	—
				0.044	—	—	—
				—	—	—	—
				—	—	—	—
DT16	Unmanned aerial vehicle	2.45	−1.887 **	—	0.221 **	—	0.356 **
			0.003	—	0.025	—	0.011
				—	0.102 **	—	0.336 **
				—	0.017	—	0.008
DT17	Cyber-physical systems (CPS)	2.98	−0.412 **	0.307 **	—	—	—
			0.001	0.031	—	—	—
				—	—	—	—
DT18	Cybersecurity	2.35	−0.678	0.221 **	—	—	—
			0.008	0.031	—	—	—
				0.121 **	—	0.222 **	—
				0.022	—	0.013	—
				—	—	—	—
DT19	Big Data analytics	2.97	−0.975	—	—	0.117 **	—
			0.002	—	—	0.009	—
				—	—	0.287 **	—
				—	—	0.022	—
				—	—	—	—
DT20	Modular construction	2.01	−2.412 **	—	—	—	—
			0.001	—	—	—	—
				—	—	—	—
				—	—	—	—
	Total (overall)			11	10	9	8
	Total (contractors only)			6	4	5	5
	Total (consultants only)			1	0	3	2

Note: Normal text = “overall”; bold = “contractors only”; and italics = “consultants only”. Only statistically significant correlations (p-value) are presented: * Sig: indicates significance at p-value < 0.05 and ** Sig: indicates significance at p-value < 0.01. ^b Values in each cell show the Wilcoxon signed-rank test statistic (upper value) and significance (lower value).

). This analysis investigated the relationship between the degrees of implementation for various DT variables and each of the four CE metrics. Three separate analyses were conducted. The first analysis considered the entire sample (n = 229) to assess the generalizability of the findings to the broader construction industry. The remaining two analyses focused solely on contractors (n = 135) and consultants (n = 94), respectively. To assess the relationship between the adoption of digital technology and the competitive edge in construction firms, two statistical tests were employed: multiple linear regression analysis (

Table 4. Results of multiple linear regression.

Dependent Variable	Independent Variable	β	σ	b	t Value	p-Value	R2	Adj R2
CE1—Efficient resource management	Constant	2.276	0.341	NA	6.587	<0.001 **	—	—
	Cloud-based technology (DT5)	0.269	0.071	0.325	3.116	0.003 *	0.173	0.171
	Artificial intelligence (DT1)	0.176	0.078	0.226	2.238	0.022 *	0.225	0.267
CE2—Real-time monitoring and control	Constant	2.432	0.226	NA	11.467	<0.001 **	—	—
	Cloud-based technology (DT5)	0.267	0.067	0.367	3.678	<0.001 **	0.147	0.136
CE3—Data-driven decision-making	Constant	2.612	0.226	NA	11.456	<0.013	—	—
	Big Data analytics (DT19)	0.278	0.078	0.322	3.176	0.001 **	0.105	0.098
CE4—Improved collaboration and communication	Constant	2.237	0.316	NA	6.187	<0.001 **	—	—
	Sensor-based technology (DT15)	0.248	0.078	0.261	3.005	0.002 **	0.125	0.126
	Internet of Things (IoT) (DT9)	0.218	0.079	0.267	2.561	0.002 **	0.184	0.163

Note: Regression Coefficient (β): This value indicates the change in the dependent variable associated with a one-unit change in the independent variable, holding all other independent variables constant. The standard error (σ) of the β coefficient reflects potential variation, if different data samples were collected. Standardized Regression Coefficient (b): This coefficient allows for a direct comparison of relative relationship strengths between independent variables and the dependent variable. The t-statistic assesses the significance of the β coefficient, while the p-value represents the probability of observing a t-statistic as extreme or more extreme than the calculated one under the null hypothesis (β = 0). A p-value less than 0.05 (*) or even stronger significance (** p-value less than 0.01) indicates a statistically significant relationship between the β coefficient and the dependent variable. R²: This coefficient of determination shows the proportion of variance in the dependent variable explained by the independent variables in the model.

) and the Mann–Whitney U test (

Table 5. Outcomes from the Mann–Whitney U test.

Code	Description	Mean Rank 1	Mean Rank 2	Z Value	Sig. (2-Tailed)
DT15	Sensor-based technology
CE1	Firms exhibiting lower versus higher efficiency in resource management	41.27	48.19	−2.219	0.025
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	33.80	48.14	−3.575	0.006
DT19	Big Data analytics
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	41.03	50.19	−3.421	0.009
CE3	Firms with low versus higher reliance on data-driven decision-making	41.05	49.89	−2.363	0.013
DT16	Unmanned aerial vehicle
CE1	Firms exhibiting lower versus higher efficiency in resource management	42.27	48.19	−2.221	0.033
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	44.13	49.22	−3.421	0.000
CE3	Firms with low versus higher reliance on data-driven decision-making	41.05	48.79	−2.363	0.001
DT5	Cloud-based technology
CE1	Firms exhibiting lower versus higher efficiency in resource management	42.37	47.29	−2.219	0.025
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	40.11	49.33	−3.421	0.008
CE3	Firms with low versus higher reliance on data-driven decision-making	42.15	47.11	−2.363	0.003
DT1	Artificial Intelligence (AI)
CE1	Firms exhibiting lower versus higher efficiency in resource management	41.28	47.22	−2.221	0.005
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	41.02	52.09	−3.321	0.009
CE3	Firms with low versus higher reliance on data-driven decision-making	39.15	48.38	−2.112	0.026
DT10	Big Data
CE2	Firms with lower versus higher utilization of real-time monitoring and control	39.39	48.15	−3.514	0.012
DT17	Cyber-physical systems (CPS)
CE3	Firms with low versus higher reliance on data-driven decision-making	41.05	48.81	−2.263	0.003
DT6	Design for manufacturing and assembly (DfMA)
CE1	Firms exhibiting lower versus higher efficiency in resource management	40.27	47.19	−2.109	0.025
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	40.03	50.19	−3.333	0.003
DT4	Robotic technology
CE2	Firms with lower versus higher utilization of real-time monitoring and control	39.39	48.15	−3.514	0.012
DT3	Building information modeling (BIM)
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	33.80	48.14	−3.575	0.006
CE2	Firms with lower versus higher utilization of real-time monitoring and control	37.09	49.77	−2.209	0.010
DT8	Machine learning
CE1	Firms exhibiting lower versus higher efficiency in resource management	40.30	46.08	−2.648	0.030
DT9	Internet of Things (IoT)
CE3	Firms with low versus higher reliance on data-driven decision-making	38.76	47.44	−2.532	0.027
CE1	Firms exhibiting lower versus higher efficiency in resource management	41.18	47.19	−2.547	0.042
CE2	Firms with lower versus higher utilization of real-time monitoring and control	47.02	50.31	−2.978	0.011
DT2	Radio frequency identification (RFID)
CE2	Firms with lower versus higher utilization of real-time monitoring and control	38.11	50.02	−2.353	0.020
CE1	Firms exhibiting lower versus higher efficiency in resource management	41.04	42.81	−2.648	0.029
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	40.63	41.96	−2.547	0.019
DT11	Computer-aided design (CAD)
CE1	Firms exhibiting lower versus higher efficiency in resource management	41.18	47.19	−2.547	0.042
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	41.03	52.19	−3.421	0.009
DT14	Computer-aided manufacturing
CE4	Firms exhibiting lower versus higher proficiency in collaboration and communication	33.80	48.14	−3.575	0.006
DT18	Cybersecurity
CE3	Firms with low versus higher reliance on data-driven decision-making	41.05	49.89	−2.363	0.013
	Utilization by contractors (n = 135) versus consultants (n = 94) in adoption	53.85	41.56	−2.117	0.002

Note: Firms with lower resource management (n = 42), lower real-time monitoring and control (n = 53), lower data-driven decision-making (n = 49) and lower ability in collaboration and communication (n = 34) tend to achieve a mean rank of 1 compared to their nearest competitor. This holds true unless otherwise specified. Conversely, firms with higher resource management (n = 55), higher real-time monitoring and control (n = 55), higher data-driven decision-making (n = 53) and higher ability in collaboration and communication (n = 61) tend to achieve a mean rank of 2 compared to their nearest competitor, unless otherwise noted.

). The results of the Mann–Whitney U test unveiled a clear trend: firms with a stronger competitive edge (CE1 to CE4) tend to adopt specific digital technologies at a significantly higher level compared to those with a weaker edge. This trend was observed across multiple competitive-edge metrics, including efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4). Technologies such as sensor-based technology (DT15), Big Data analytics (DT19) and artificial intelligence (DT1) were particularly influential, showing higher adoption in firms with greater operational efficiency and strategic capabilities. Moreover, the data indicated that firms with a more advanced digital technology adoption exhibited substantial improvements in their ability to manage resources, leverage real-time data, make informed decisions and foster better communication and collaboration within teams.

However, the correlation analysis, as shown in Table 3, indicates that four specific technologies—augmented/virtual/mixed reality (DT7), geographic information systems (DT12), 3D printing/additive manufacturing (DT13) and modular construction (DT20)—demonstrated no significant correlation with any of the competitive-edge metrics. This suggests that their adoption might not be as crucial for immediate gains. On the other hand, 16 other technologies exhibited significant correlations (p < 0.05) with one to four competitive-edge metrics. Among these, artificial intelligence (AI) (DT1) and cloud-based technology (DT5) stand out as “high impact technologies” due to their strong association (with at least three metrics) with a firm’s competitive edge.

The three converging statistical tests provide a clear picture: cloud-based technology (DT5) and artificial intelligence (AI) emerge as the frontrunners among digital advancements for heavy-construction firms. The results suggest a positive relationship between cloud-based technology and efficient resource management (CE1) and real-time monitoring and control (CE2). Specifically, for every one-unit increase in the adoption of cloud-based technology, there is a corresponding increase in efficient resource management and real-time monitoring and control. This implies that leveraging cloud-based technology can enhance resource management and monitoring practices in construction projects, leading to improved efficiency. The findings also indicate a significant positive association between artificial intelligence and efficient resource management (CE1). This suggests that the integration of artificial intelligence tools and techniques can enhance resource management processes within construction projects, potentially optimizing resource allocation and utilization. Moreover, the adjusted R-squared values provide insights into the proportion of variance in the dependent variable explained by the independent variables in each regression model, while considering the number of predictors included. For instance, the adjusted R-squared value for the model predicting efficient resource management (CE1) is 0.171, suggesting that approximately 17.1% of the variance in efficient resource management can be explained by the predictors included in the model, namely cloud-based technology and artificial intelligence. Similarly, the adjusted R-squared values for other construction efficiency factors, such as real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4), are 0.136, 0.098 and 0.126, respectively. These adjusted R-squared values indicate the extent to which the chosen independent variables contribute to explaining the variability observed in the respective construction efficiency factors. Through the synthesis of data from Table 3 and Table 4, we can address the research question of which digital technologies contribute to a higher competitive edge for heavy-construction firms. The answer lies in the adoption of cloud-based technology (DT5) and artificial intelligence (AI) (DT1).

4.4. Model for the Adoption of Digital Technologies

Formulating a digital technology (DT) adoption model was helpful in achieving the third objective of this study (see

Table 6. Competitive edge levels determined by significant correlations with metrics (CE1 to CE4) and adoption level.

Level of Competitive Edge	Digital Technologies to Be Adopted
Excellent	Row 1—Adopt intensively
	CE1, CE2, CE3, CE4
4 (overall)	—
4 (contractors)	—
4 (consultants)	—
Very good	Row 2—Adopt steadily
	CE1, CE2, CE3		CE1, CE2, CE4		CE1, CE3, CE4			CE2, CE3, CE4
3 (overall)	DT5		—		DT9			DT1, DT5
3 (contractors)	—		—		—			—
3 (consultants)	—		—		—			—
Good	Row 3—Adopt cautiously
	CE1 & CE2	CE1 & CE3		CE1 & CE4		CE2 & CE3	CE2 & CE4		CE3 & CE4
2 (overall)	DT15	DT4, DT8, D10		DT14		DT2	DT11, DT16		DT3, DT6
2 (contractors)	DT5	DT4, DT18		—		—	—		DT3
2 (consultants)	—	—		—		—	—		—
Low	Row 4—Adopt after extensive evaluation
	CE1	CE2		CE3		CE4
1 (overall)	DT17	DT9		DT19		—
1 (contractors)	DT15	—		—		—
1 (consultants)	—	—		—		—
None	Row 5—Not crucial for immediate gains
0 (overall)	DT7, DT12, DT13 & DT20
0 (contractors)	DT1, DT7, DT12, DT13, DT17, DT20
0 (consultants)	DT1, DT2, DT3, DT4, DT7, DT8, DT9, DT11, DT12, DT13, DT14, DT15, DT17, DT18, DT20

Table created by the authors.

). This model guides construction firms in selecting specific technologies for implementation. Drawing upon the theoretical framework and the statistical findings, the model assigns each DT to a designated cell based on its correlation with competitive-edge metrics (CE1 to CE4). The model presents different levels of detail stemming from three correlation analyses: overall, contractors only and consultants only. The model’s highest level of detail categorizes DTs based on the results for the entire sample (n = 229), making it applicable to the construction industry as a whole (refer to the top section of Table 6). Subsequent sections delve deeper, providing more granular adoption models specific to contractors only (n = 135, bolded in Table 6) and consultants only (n = 94, italicized in Table 6). DTs positioned in the top row of the model exhibit excellent competitive advantages. This is because they display significant correlations with all four CE metrics. Thus, these technologies warrant prioritization for adoption. Moving down the table, technologies in the second, third and fourth rows demonstrate progressively weaker correlations, aligning with three, two and one CE metric, respectively. This suggests a more cautious and well-considered approach to their adoption. Rounding out the model, the bottom row (Row 5) houses DTs that lack significant correlations with any competitive-edge metric. This placement signifies a need for further investigation before adoption.

5. Discussion

5.1. Digital Technologies for Intensive Adoption in Heavy-Construction Firms

Table 3 and Table 6 reveal that the second row of the adoption model encompasses key technologies such as cloud-based technology (DT5) and artificial intelligence (AI) (DT1). These technologies exhibit a significant correlation with three competitive-edge metrics (CE2, CE3 and CE4). Notably, although cloud-based technology (DT5) has been regarded as a super technology in previous sections of this work, it is typically utilized in conjunction with other digital technologies rather than in isolation. For instance, when paired with sensor-based technology, cloud computing enables efficient resource management by centralizing project data and facilitating real-time collaboration among stakeholders [19]. This centralized approach streamlines resource allocation, minimizes wastage and enhances project efficiency. Moreover, combining cloud-based technology with Internet of Things (IoT)-based devices enables real-time monitoring and control of construction processes and equipment. According to [31], by collecting and analyzing data from IoT sensors deployed across construction sites, cloud platforms can provide actionable insights into equipment performance, safety conditions and environmental factors. This real-time monitoring capability empowers project managers to proactively identify issues, optimize workflows and ensure timely project delivery [32]. Furthermore, cloud-based technology serves as a foundational infrastructure for implementing advanced analytics and machine learning algorithms, enabling data-driven decision-making in construction projects [33]. By leveraging cloud-based Big Data analytics platforms, heavy-construction firms can analyze vast amounts of project data to identify trends, predict outcomes and optimize project performance [34].

The finding of a significant correlation between AI and real-time monitoring and control (CE2) aligns with and extends the work described in [14]. This strengthens the notion that AI can significantly streamline construction project management by facilitating real-time data collection and analysis. By integrating AI-powered systems for real-time monitoring and control, construction teams can gain deeper insights into project dynamics, anticipate potential issues and optimize resource allocation in a proactive manner. This correlation suggests that AI plays a crucial role in enhancing project visibility, agility and responsiveness, ultimately leading to improved project outcomes and stakeholder satisfaction [35]. Also, the utilization of AI-driven solutions enables construction firms to stay competitive in an increasingly complex and dynamic industry landscape. The finding that AI adoption is significantly correlated with data-driven decision-making (CE3) also echoes the insights in [36]. According to this study, integrating AI technologies into construction project management processes facilitates the collection, analysis and interpretation of vast amounts of project data in real-time. This enables stakeholders to make informed decisions based on accurate insights and predictive analytics, leading to more efficient resource allocation, risk mitigation and project scheduling [6].

Another major finding from Table 6 is the cautious approach of several technologies in heavy-construction projects, which aligns with the dynamic nature of technology adoption in the industry. Row 3, labeled “Adopt cautiously”, indicates that certain digital technologies are approached with prudence. This cautious approach is particularly evident in the adoption of sensor-based technology (DT15), reflecting its known correlations with efficient resource management (CE1) and real-time monitoring and control (CE2). This finding is consistent with prior studies emphasizing the role of sensor technology in optimizing resource utilization and enhancing project control [37]. However, the cautious stance toward certain digital technologies, such as building information modeling (BIM) (DT3) and design for manufacturing and assembly (DfMA) (DT6), contradicts the prevailing discourse advocating for their extensive adoption due to their transformative potential in project planning and efficiency improvement [38]. This discrepancy underscores the careful consideration required when implementing new technologies in heavy-construction projects, in which factors such as project complexity and organizational readiness play significant roles. Additionally, the adoption of robotic technology (DT4), machine learning (DT8) and Big Data (DT10) is associated with efficient resource management (CE1) and data-driven decision-making (CE3). However, the cautious approach to radio frequency identification (RFID) technology (DT2) contradicts its recognized benefits in enhancing construction site visibility and logistics management [39].

While some digital technologies, such as augmented reality (AR) and virtual reality (VR) (DT7), were identified as being less strongly correlated with CE metrics, this observation warrants further exploration. The cautious adoption of these technologies may stem from their relatively niche applications within heavy construction, with perceived benefits being more indirect or longer-term compared to technologies like AI or cloud computing. AR and VR are often viewed as valuable tools for training, design visualization and collaboration; however, their impact on immediate operational efficiency or resource management might not be as apparent to industry stakeholders [34]. Additionally, the high initial costs, lack of standardized implementation frameworks and potential resistance from workers unfamiliar with these technologies could limit their adoption and integration into routine construction practices [6]. This cautious approach could be a contributing factor to the lower association of AR and VR with competitive advantage, as firms may prioritize technologies that provide more immediate, tangible benefits such as improved project monitoring or enhanced decision-making capabilities. As industry familiarity with AR and VR grows, and as the technologies become more cost-effective and integrated, their potential for contributing to competitive advantage in heavy construction may increase.

5.2. Surprising Discoveries

One surprising discovery from Table 6 is in row one, where no digital technologies are listed for intensive adoption by both contractors and consultants in heavy-engineering projects. This absence raises questions about the traditional notions of the technology adoption patterns in the construction industry. Typically, in sectors marked by intense competition, one would anticipate a high level of digital technology adoption to maintain a competitive edge. However, the lack of specified technologies suggests that there exists certain issues that might be preventing the intensive usage of digital technologies. Such an absence could indicate various underlying issues. Firstly, it might suggest a reluctance or hesitancy among industry stakeholders to invest in digital solutions due to concerns about the effectiveness, compatibility with existing systems, or return on investment associated with these solutions [40]. Additionally, it could point to a lack of awareness or understanding of the potential benefits that digital technologies can offer in improving efficiency, productivity and overall project outcomes [33]. Moreover, regulatory barriers, organizational inertia, or resource constraints may be hindering the widespread adoption of digital technologies in heavy-engineering projects [5]. Whatever the reasons may be, addressing the underlying issues preventing the adoption of digital technologies is crucial for the construction industry to remain competitive, innovative and sustainable in the rapidly evolving digital landscape.

Another unexpected finding from Table 6 is the prevalence of cautious adoption among consultants across multiple rows. This trend suggests a degree of conservatism among consultants in embracing digital technologies for heavy-engineering projects. This caution may stem from various factors such as prioritizing proven traditional methods over adopting digital technologies, fearing potential disruptions or complications during project execution [33]. Additionally, consultants in heavy-construction projects often work within complex regulatory frameworks and contractual obligations, which may necessitate a more cautious approach to adopting unfamiliar digital tools to ensure compliance and mitigate legal risks [20]. Furthermore, the nature of heavy-construction projects, characterized by their long duration, complex logistics and substantial investments, may require consultants to deeply evaluate the suitability and feasibility of digital technologies before integrating them into existing workflows. Nigeria’s long-standing infrastructure challenges, including unreliable power supply and limited internet connectivity in some areas, may also hinder the effective implementation of digital technologies [16]. As such, consultants may be hesitant to adopt technologies that rely heavily on stable electricity and internet access, as disruptions could disrupt project workflows and communication channels.

The findings described in Table 6 also reveal that there are certain technological solutions that are not considered imperative for achieving a competitive edge or enhancing project performance in the short term. This finding challenges the conventional wisdom that the technologies accompanying the fourth industrial revolution are essential for driving efficiency and productivity in construction projects. This raises questions about the perceived value and impact of certain digital tools and highlights the need for a deeper understanding of the specific needs and contexts within the heavy-engineering sector. It also underscores the importance of targeted investment in technologies that offer tangible benefits and align closely with project objectives. Additionally, this finding prompts a reevaluation of the criteria used to assess the relevance and applicability of digital solutions in construction, emphasizing the necessity of a more context-aware approach to technology adoption. This finding also underscores the importance of prioritizing technology investments based on their specific relevance and potential to deliver tangible benefits rather than adopting them indiscriminately.

6. Practical Implications of the Digital-Technology-Adoption Model

The findings of this study reveal that digital technologies such as cloud-based technology and artificial intelligence (AI) are significant drivers of competitive advantage in heavy-construction firms. This aligns with previous studies that highlight the positive impacts of digital technologies on construction efficiency and competitiveness [13,17]. Construction industry practitioners should therefore prioritize the adoption and integration of these technologies into their workflows to remain competitive and meet evolving project demands. Similar findings have been reported in research from developed countries, where the adoption of AI and cloud-based systems has led to increased productivity and improved decision-making (e.g., [22]). Moreover, the cautious approach observed among consultants in this study suggests the importance of addressing barriers such as regulatory constraints, infrastructure challenges and organizational inertia to facilitate smoother technology-adoption processes. These challenges are consistent with findings from both developed and developing countries, where lack of infrastructure and resistance to change have been cited as major obstacles to digital technology integration in construction (e.g., [15]).

Additionally, the identification of certain technologies as non-crucial for immediate gains underscores the need for a deeper understanding of technology relevance and applicability in construction projects. While these technologies may not directly impact competitive-edge metrics in the short term, they could still offer long-term benefits if strategically integrated into the organization’s technology roadmap. Previous studies have also pointed out that while the impact of some technologies may not be immediately apparent, their strategic use can contribute to long-term organizational growth (e.g., [37]). Therefore, stakeholders should carefully evaluate the specific needs and contexts of their projects before investing in digital solutions, focusing on technologies that offer tangible benefits aligned with project objectives. This echoes the recommendation in the existing literature to tailor technology adoption to the specific challenges and goals of construction firms (e.g., [38]).

The adoption model also holds significant practical value within the construction industry, particularly in benchmarking and strategic decision-making related to digital technology integration in heavy-engineering projects. Table 6 of this model offers tailored insights, with distinct sections addressing overall industry findings, as well as those specific to contractors and consultants. To bolster competitiveness, construction firms can reference the relevant sections aligned with their operational scope to assess their standing and identify areas for improvement. For instance, consider a construction firm currently utilizing robotic technology (DT4) yet falling short in achieving efficient resource management (CE1) and data-driven decision-making (CE3) compared to its closest competitor. In such a scenario, the firm should delve into the reasons behind this performance gap to understand why the desired outcomes have not been realized. This mirrors the approach suggested by [29], which emphasizes the need for firms to conduct in-depth assessments to determine why certain technologies underperform.

The model can also serve as a guide for prospective adopters, directing them on which digital technologies to invest in to gain a competitive edge. Research on digital technology adoption in construction, such as that by [20], has shown that a clear roadmap for technology integration is crucial for firms looking to remain competitive in the rapidly evolving sector.

7. Conclusions and Areas for Future Studies

The primary aim of this study was to assess the influence of digital technology adoption on the competitive edge (CE) of heavy-engineering firms in Lagos State, Nigeria. To achieve this, a quantitative research approach was employed. A well-structured questionnaire was disseminated through an online survey, targeting construction professionals who were conversant with their firm’s adoption of digital technologies. The survey yielded 229 usable responses, with participation from both contractors (135) and consultants (94) within Lagos State. Key findings emerged from the data analysis. Firstly, the study revealed a significant level of adoption for specific digital technologies, with artificial intelligence (AI), cloud-based technology and the Internet of Things (IoT) showing particularly high adoption rates, as revealed in Table 3. Secondly, a strong correlation was identified between the adoption levels of certain technologies and metrics associated with a competitive edge which was presented in Table 3. Notably, two digital technologies—AI and cloud-based technology—were found to be significantly associated with achieving the pre-defined competitive-edge metrics like efficient resource management, real-time monitoring and control, data-driven decision-making and improved collaboration and communication within construction firms.

This research offers a significant contribution to the field by formulating a digital-technology-adoption model (presented in Table 6). This model goes beyond a simple listing of technologies, and functions as a benchmarking tool for existing adopters. By comparing their current adoption levels with the model’s benchmarks, heavy-construction firms can pinpoint areas for improvement and strategically utilize digital technologies to enhance their competitive edge. For instance, a company might be extensively utilizing computer-aided design (CAD) (DT11), but lack robust real-time monitoring and control or collaboration practices, as suggested by the adoption model in Table 6. Thus, this insight can guide them to reengineer their processes to achieve a more competitive advantage. The adoption model may also be beneficial for firms considering initial forays into digital technologies. By using the model as a roadmap, these firms can prioritize which technologies to implement first, focusing on those most demonstrably linked to achieving key competitive advantages like efficient resource management and improved collaboration. Furthermore, the model can be a valuable tool for future research, providing a foundation for comparative studies across different regions or project types.

While this study has made significant contributions, several limitations must be acknowledged. Firstly, certain metrics crucial for assessing competitive edge, such as profitability, were not included in the analysis. Given profitability’s importance in gauging a firm’s financial health and market success, future research should aim to integrate such metrics for a more comprehensive evaluation of the impact of digital technology adoption on firm performance. Moreover, the study focused solely on heavy-engineering firms in Lagos State, Nigeria, potentially limiting the generalizability of the findings to other regions or industry sectors. To address this, future studies should explore digital-technology-adoption implications across diverse contexts to validate findings and identify potential variations in impact. Another limitation arises from the subjectivity inherent in respondents’ selection of interaction levels with digital technologies. Clearer definitions or criteria for each option, particularly for options denoting modest utilization and regular engagement, are needed to ensure consistency in responses. Integrating qualitative methods, such as interviews or focus groups, could further enrich understanding by providing deeper insights into respondents’ perceptions and experiences. Additionally, the adoption model developed in this study was based on correlations rather than causal relationships, necessitating careful interpretation of the findings. To address this, future research could employ longitudinal or experimental designs to investigate causality more rigorously. Finally, the choice of purposive sampling limits the external validity by potentially skewing results to reflect perspectives prevalent within a specific demographic or region. This may not be representative of broader industries or geographic areas. Future studies might want to adopt a more comprehensive sampling strategy, such as stratified or random sampling across multiple regions and industry sectors, to enhance generalizability and provide a broader understanding of digital technology adoption in heavy-engineering projects.