Managerial perceptions of emerging technology adoption in construction: evidence from developing countries

The construction industry is undergoing a transformative phase known as Construction 4.0, driven by the integration of emerging technologies. In a context where organizations aim to enhance productivity, efficiency and competitiveness, digital transformation has become a strategic imperative (Karmakar and Delhi, 2021). Emerging technologies are transforming construction processes by enhancing coordination, enabling real-time visibility and supporting data-driven decisions, ultimately improving efficiency and sustainability (Souza and Debs, 2023).

Cloud computing and data analytics for storage, sharing and analysis enable the foundation for technological modernization (Marzouk and Enaba, 2019). When paired with AI and RPA, they enhance forecasting, risk control and operational efficiency while easing collaborative work (Aghimien et al., 2021). Yet, adoption lags because of limited resources, weak digital systems and insufficient investment in innovation (Kissi et al., 2023; Souza and Debs, 2023).

Research on emerging technology adoption in construction is limited in developing countries and often fragmented, focusing on single tools or developed contexts (Aghimien et al., 2021; Kissi et al., 2023). Little is known about how managers perceive and lead the simultaneous adoption of multiple technologies, highlighting the need for more integrated investigations.

To address this gap, the present study examines the adoption of cloud computing, data analytics, AI and RPA in the construction sector of developing countries, guided by the technology–organization–environment (TOE) framework (Tornatzky et al., 1990). This framework enables a holistic evaluation of technological readiness, organizational support and environmental pressures in shaping adoption and impact (Na et al., 2022). Thus, this study addresses this gap by providing cross-country managerial insights into the determinants shaping the adoption of AI, data analytics, cloud computing and RPA in developing construction and related industries.

The originality of this research lies in its integrative approach, focusing on multiple emerging technologies and assessing their adoption from the perspective of managers in developing countries (Souza and Debs, 2023). The study contributes theoretically by extending TOE to construction contexts in resource-constrained regions and practically by informing strategies for effective technology deployment. The paper is structured as follows: Section 2 reviews the literature, Section 3 details the methodology, Section 4 presents results, Section 5 discusses findings and Section 6 concludes with limitations and directions for future research.

Innovative tools, methods and techniques that are either in the development stage or just beginning to be used and have the potential to influence a variety of industries, including construction, are referred to as emerging technologies (Chowdhury et al., 2019). These technologies seek to improve process efficiency, productivity and safety by leveraging advances in digital capabilities. Emerging technologies in the construction sector include, but are not limited to, robotics, 3D printing, AI, the Internet of Things (IoT) and building information modeling (BIM) (Gamil et al., 2020; Karmakar and Delhi, 2021; Souza and Debs, 2023). On the other hand, processes are the organized procedures and methodical workflows that govern how construction activities are carried out. Throughout the project lifecycle, these encompass crucial stages like planning, designing, implementing and managing the project, ensuring effectiveness, uniformity and conformity to industry standards (Elghaish et al., 2022). However, several factors, such as organizational preparedness, perceived advantages and current obstacles, affect the intention to use these technologies.

Sustainability and resource efficiency are key drivers of technology adoption in construction, as Industry 4.0 digitalization can enhance resource use and project sustainability (Sajjad et al., 2023). Effective Construction 4.0 implementation requires mission-oriented innovation, aligned strategies and proactive staff training (Lekan et al., 2021). Such structured approaches help firms address challenges related to resistance and limited training (Aghimien et al., 2022; Perrier et al., 2020). Although BIM improves efficiency, many firms still encounter notable adoption barriers (Bamgbose et al., 2024), highlighting the need for targeted strategies across different industry segments. Leadership and organizational culture are also pivotal, with innovation-supportive environments and constructive leadership styles enabling smoother adoption (Na et al., 2023). Stakeholder perceptions of benefits and risks further shape adoption choices (Osunsanmi et al., 2020), including concerns about scalability and security (Singh, 2024). Skill shortages remain a major obstacle, making updated education and training essential for building a capable workforce (Ogunseiju et al., 2023). Integrating digital tools has become a strategic necessity, as AI and big data can enhance outcomes when aligned with existing workflows (Aghimien et al., 2022). Adoption is also slowed when technology is viewed as a cost rather than an investment (Celik et al., 2024; Kissi et al., 2023). Supportive government programs can reduce risks (Soltani et al., 2023)., while collaboration among industry stakeholders promotes integration (Dolla et al., 2023). Fully leveraging digital twins requires strong data infrastructure and continuous learning (Aghimien et al., 2021; Park et al., 2024; Sepasgozar et al., 2023; Souza and Debs, 2023).

According to Souza and Debs, 21 emerging technologies are crucial for the shift to Construction 4.0. Design tools (BIM, 3D printing, VR); integrated systems (robotics, life cycle analysis); data-driven technologies (AI, IoT, RPA) (Souza and Debs, 2023). Cloud computing supports collaboration through BIM and enables AI-driven analytics (Zhang et al., 2022). Analytics and machine learning improve project monitoring, efficiency and predictive maintenance (Baduge et al., 2022; Pour Rahimian et al., 2020). AI supports automation of supply chains, scheduling and generative design (Elkhapery et al., 2023). AI combined with robotics enables repetitive and hazardous tasks automation (Ha et al., 2019). IoT and edge computing enable smart, autonomous construction sites, improving productivity and safety (Aghimien et al., 2019; Onososen and Musonda, 2023).

The TOE framework provides a foundational perspective for understanding how construction firms adopt new technologies. Introduced by Tornatzky and Fleischer (Tornatzky et al., 1990), it explains adoption as the outcome of interactions among technological, organizational and environmental conditions. This framework is particularly relevant to construction, where complex processes and evolving project environments require structured analysis of factors that encourage or hinder innovation (Tornatzky et al., 1990). For instance, research by Lekan et al. highlights how Construction 4.0 can foster inclusive industrial growth and sustainable innovation, aligning with the TOE view that strong technological capabilities accelerate adoption (Lekan et al., 2021). Organizational aspects, such as leadership commitment, cultural readiness and resource availability, also play a decisive role. Yigitbasioglu’s findings similarly show that managerial support and institutional pressures strongly shape decisions to adopt cloud-based technologies (Yigitbasioglu, 2015), emphasizing the need for supportive work environments. Wernicke et al. demonstrate this through a digital maturity model that links external conditions to firms’ digital capabilities (Wernicke et al., 2023). Overall, the TOE framework serves as a valuable tool for examining readiness, capability and context shape technology uptake. As construction continues to digitalize, applying this framework will help organizations strengthen innovation efforts and manage technological integration effectively (Gutierrez et al., 2015; Na et al., 2023; Naji et al., 2024; Sepasgozar and Davis, 2018; Taneja et al., 2024).

To conclude, to the best of the authors’ knowledge, existing literature lacks quantitative methodologies for studying rising emerging technology adoption in the construction sector, particularly from the perspective of managers in developing countries. To address this gap and build upon past research, the TOE framework offers a thorough lens to understand the factors influencing technology adoption. It can be used to analyze the adoption of emerging technologies in the construction sector. The framework considers three crucial contexts when making decisions about the integration of new technologies: technological, organizational and environmental factors. Moreover, the impact of emerging technology adoption on both managers and the construction sector needs to be investigated. Hence, this study gives light on the actual adoption of the technologies mentioned and raises stakeholders’ and enterprises’ knowledge of the impact of certain components on the uptake of these emerging technologies in the construction sector.

The research follows a structured process beginning with an extensive literature review to build the study’s conceptual base and inform questionnaire development. A systematic search is carried out using Scopus, Google Scholar and ScienceDirect, which are highly effective for comprehensive evidence retrieval (Bramer et al., 2017). Keywords such as “cloud computing,” “data analytics,” “artificial intelligence,” “robotic process automation,” “emerging technologies,” “construction” and “adoption” guide the search. Filters are applied to include peer-reviewed, English-language studies published between 2014 and 2025. After manual screening, 58 relevant articles are selected, and most measurement items used in the study are adapted from established technology adoption research.

The study investigates how evolving technologies influence managerial practices and the future of the construction sector in developing countries. The TOE framework provides the analytical basis for assessing adoption, emphasizing technological, organizational and environmental dimensions (Tornatzky et al., 1990). Technological factors include perceived usefulness, system compatibility and complexity. Organizational factors involve internal capabilities such as leadership support, culture and skilled personnel. Environmental influences involve regulatory pressures, market conditions and competitive forces. Prior research has widely applied the TOE framework to examine emerging technologies in construction (Bamgbose et al., 2024; Elghaish et al., 2022; Gutierrez et al., 2015; Na et al., 2023; Naji et al., 2024; Sepasgozar and Davis, 2018; Taneja et al., 2024). Collectively, these dimensions shape firms’ intentions to adopt emerging technologies. Table 1 maps each of our survey questions and variables to their corresponding TOE dimensions.

Accordingly, the following hypotheses are proposed:

All constructs in this study were specified as formative because each is defined by multiple distinct indicators that collectively shape its meaning. The components of perceived ease of use, relative advantages, improved management, financial incentives, competitive pressure and regulatory constraints represent diverse dimensions that do not necessarily covary but instead function as complementary inputs forming the overall construct. Removing any indicator would alter the conceptual domain of the construct, confirming that the indicators act as causes rather than consequences of the latent variable. This conceptualization aligns with the multidimensional and context-dependent nature of managerial perceptions in technology adoption, making a formative specification theoretically appropriate.

Hence, these factors are inserted in the survey to find answers to the research question of this study. Figure 1 summarizes the TOE dimensions along with the items inserted in the survey and used for further analysis in this research.

Thus, this paper’s primary research questions are:

To address the RQs, an online survey is conducted. The survey method is chosen to collect a large volume of quantitative data from a diverse sample, enabling comparative and statistical analysis using descriptive and inferential methods. The questionnaire is developed and validated through pilot interviews with three industry experts, each with over ten years of experience. These interviews help improve the questions, ensuring their clarity and relevance for validating survey instruments in construction industry research (Li et al., 2011). Participants include managers in diverse roles across construction companies and related sectors in developing countries. Data are collected via Google Forms, with the survey distributed to a targeted audience from December 2024 to February 2025. The questionnaire has a screening question at the outset to make sure that only the right people with the necessary experience and responsibility completed the survey. Respondents who were not managers from the construction or related sectors or who provided incomplete responses were excluded, resulting in a final sample of 325 participants out of 405 for SEM analysis. Missing data were addressed using the default PLS-SEM algorithm, which estimates model parameters based on all available information without discarding partially completed cases. To assess potential common method bias due to the self-report survey design, we conducted both Harman’s single-factor test and full collinearity VIF analysis. The single-factor test indicated that no single factor accounted for the majority of variance, and all VIF values were below 3.3, suggesting that common method bias is unlikely to have affected the results (Kock et al., 2012). Outliers were examined using Mahalanobis distance, and no cases exceeded the critical threshold, so all observations were retained (Mclachlan, 1999).

The survey was distributed online via email and LinkedIn to reach the targeted audience in developing countries: Middle East and Africa, leveraging the cost-effectiveness and flexibility of digital data collection. Several managers of construction companies directly shared employee email lists, while a LinkedIn search using the keyword “Construction” was used to maximize respondent reach. A snowball sampling approach was applied, with initial contacts asked to forward the survey within their professional networks. Although this method facilitated access to a broad pool across multiple developing countries, it lacks formal stratification by country, industry or demographics, which may limit sample representativeness and generalizability. Moreover, in this study, developing countries are defined according to the World Bank’s country classification system, focusing on lower-middle and upper-middle income economies (World Bank, 2025). Efforts were made to circulate the survey among diverse professional groups to mitigate potential bias, with participation voluntary and uncompensated. Following the 10 × rule for PLS-SEM, the minimum required sample size was determined based on the latent construct with the maximum number of incoming paths, which in this model is six paths. Therefore, a minimum sample of 60 participants was required. Our final sample of 325 participants exceeds this threshold, ensuring adequate statistical power for model estimation (Hair et al., 2011). The survey is accompanied by a brief overview that highlights the advantages of the research findings, describes the study’s goals and methods and guarantees the anonymity of participants and their businesses. It consists of four sections. The first section includes demographic and general information questions. To account for potential contextual influences on adoption behavior, the model included several control variables based on respondents’ characteristics and organizational context. Specifically, managerial role, years of experience, region and firm size were added as controls predicting the intention to adopt emerging technologies. These variables were selected because they may influence exposure to technology, decision-making authority or organizational readiness in construction environments. The second section captures respondents’ opinions and perceptions on the emerging technologies reviewed; this includes a self-assessed level of knowledge of these technologies and expertise level working with these technologies, perceptions of the impact of these technologies on the future of the construction profession and managers, and the main barriers to adopting these technologies. The third section assesses the organization’s means to adopt emerging technologies and the factors influencing these intentions. The fourth section investigates the usage of any other emerging technology not listed in the survey.

The knowledge and experience of the four emerging technologies: AI, RPA, cloud computing and data analytics are assessed using a six-point Likert scale with endpoints of “none” (1) and “very high” (6). The impact is assessed using a five-point Likert scale with endpoints of “no impact” (1) and “significant impact” (5). For the barriers, a list of suggestions is presented with an additional “other” option to state any obstacle not mentioned. The respondents are asked if their organizations intended to adopt one or more of the technologies reviewed. If they answer “none” or “do not know,” no further data were collected from these respondents in this section, and they move to the last section. If they answer yes, a five-point Likert scale question assessing the factors influencing the intentions to adopt of the emerging technologies are listed with endpoints “strongly disagree” (1) and “strongly agree” (5). Then a list of suggestions stating some incentives to adopt emerging technology is presented with an “other” option to extract expected barriers. The last section assesses the usage of any other emerging technology, with its application and recurrence. By giving respondents a range of choices, the Likert scale is a well-known psychometric instrument for assessing attitudes, opinions or perceptions. By converting qualitative data into a numerical format that can be readily evaluated using statistical techniques, they enable researchers to quantify subjective experiences (Stratton, 2018). According to Grosz et al., a five-point Likert scale usually provides a range of answers from “strongly disagree” to “strongly agree,” along with a neutral midway that enables participants to express neither agreement nor disagreement (Grosz et al., 2017). A six-point Likert scale, on the other hand, removes the neutral middle, forcing respondents to select an answer that could increase the decisiveness of their answers.

The dependent variable corresponding to a firm’s intention to adopt emerging technology aggregates the examined technologies into a single construct for both conceptual and practical reasons. Although each technology possesses distinct characteristics and capabilities, there is potential for overlap and interconnection. As described earlier, AI may complement and integrate with other emerging technologies under consideration. While the individual benefits of each technology are noteworthy, it is their synergistic deployment that holds the potential to drive transformative changes within the industry. Moreover, examining each of the four technologies in isolation could introduce several methodological challenges. These include heightened complexity in both data analysis and interpretation, particularly in cases where firms exhibit intentions to adopt multiple technologies simultaneously, yet the influencing factors differ across them. Such circumstances would hinder the derivation of a unified conclusion. Finally, administering a separate set of survey items for each technology would substantially increase survey length, which is likely to reduce response rates and compromise data quality. The analysis used SEM to estimate relationships. The statistical analysis was conducted using SmartPLS.

The demographic details of survey respondents show that 30% work in related fields, while the majority (70%) are employed in the construction industry. Among respondents, 51% are project managers, with the remaining distributed as follows: structural engineers (10%), ERP managers (8%), operations managers (5%), mechanical and electrical engineers (4%), CEOs (3%) and other roles (19%). In terms of education, 49% have a master’s degree, 31% have a bachelor’s degree, 9% have a diploma, 5% have a bachelor’s degree in engineering, 5% have a doctorate and 2% have a bachelor’s degree in technology. Also, 33% have less than five years of experience, 26% have more than ten years and 41% have five to ten years. Respondents are grouped geographically as follows: Middle East accounting for 59%, West Africa representing 23% and Southern Africa making up 18% of the sample. Table 2 shows the detailed distribution by country. The distribution of company sizes reveals that 32% of workers are employed by companies with 11–50 people, 25% by organizations with 500 or more, 18% by companies with 1–10 employees, 17% by companies with 100–499 employees and the smallest percentage (8%) by companies with 51–99 employees.

The study examined participants’ knowledge and professional experience in emerging technologies, including data analytics, cloud computing, RPA and AI. Results indicate that 50% of respondents reported moderate to very high knowledge in data analytics, followed by AI (47%), cloud computing (42%) and RPA (27%). Practical experience was generally lower, with 41% in data analytics, 34% in cloud computing, 29% in AI and 14% in RPA. While knowledge and proficiency levels were broadly aligned, functional proficiency tended to be ranked slightly lower than knowledge.

Respondents highlighted a significant positive influence of these technologies on future managerial roles. Data analytics was rated as having a high to major influence by 68% of participants, AI by 63%, cloud computing by 53% and RPA by 46%. Conversely, a minority perceived little to no impact: 11% for data analytics, 16% for AI, 18% for cloud computing and 32% for RPA. Similar trends were observed regarding sectoral benefits, with 70% seeing high impact from AI and data analytics, 60% from cloud computing and 55% from RPA. Key obstacles to adoption included lack of technical knowledge (27%) and high upfront costs (24%), while regulatory restrictions were least cited (6%). Ethical concerns with AI were noted by 8%, and resistance to change by 18%. Overall, 52% indicated intentions to implement emerging technologies in their organizations.

Factors influencing adoption intentions included enhanced organizational and operational management (64%), technology ease of use (59%), competitive pressures (53%), relative advantages (47%), financial incentives (43%) and regulatory constraints (41%). Regional differences were observed: Middle Eastern respondents reported higher adoption intentions, likely due to stronger digital infrastructure, government-led modernization and exposure to Industry 4.0 technologies. Ease of use and relative advantages were consistently influential across regions, while financial incentives and regulatory constraints varied; West African participants perceived stronger regulatory limitations and fewer incentives, whereas Southern African respondents reported moderate competitive pressures but lower expectations of management improvements. These findings suggest that infrastructure, policy support and digital readiness shape managerial attitudes toward emerging technologies.

The study also assessed the reliability and validity of the measurement model. Internal consistency was confirmed using Cronbach’s alpha and composite reliability, with values exceeding the 0.70 threshold (Hair et al., 2021). Convergent validity was established, as the average variance extracted (AVE) for each construct surpassed 0.50, and discriminant validity was confirmed by demonstrating that the square root of each construct’s AVE exceeded its correlations with other constructs. Measurement items loaded more strongly on their intended constructs than on others (Hair et al., 2021), as detailed in Table 3. Overall, these results indicate that knowledge, perceived benefits, organizational support and regional digital maturity significantly influence emerging technology adoption in construction, while practical experience and structural challenges moderate these intentions.

The square roots of the AVE values (diagonal entries) are greater than their corresponding inter-construct correlations. In addition to Cronbach’s alpha and composite reliability, we report Dijkstra–Henseler’s rho_A for all constructs. All rho_A values range from 0.780–0.910, exceeding the 0.70 threshold, indicating satisfactory internal consistency. These results confirm the reliability of the measurement model. Furthermore, we checked correlations among constructs for multicollinearity. The highest correlation was 0.814 between environmental and technological, below the 0.85 threshold. Therefore, all constructs are sufficiently distinct, and the structural model can be reliably assessed. These results appear in Table 4.

The results demonstrate that all measurement items have loadings above the 0.60 threshold (Hair et al., 2021) on their respective constructs. While some cross-loadings slightly exceed 0.40, the difference between primary and secondary loadings is above the 0.30 threshold (Hair et al., 2021), supporting good discriminant validity for all constructs. Additionally, the independent variables’ variance inflation factor (VIF) values are examined. These findings suggest that multicollinearity is not a cause for concern (Hair et al., 2021). To further strengthen this assessment, discriminant validity was tested using the heterotrait–monotrait ratio (HTMT) (Henseler et al., 2015). As shown in Table 5, most HTMT values remained below the conservative 0.85 threshold. One pair, ease of use–regulatory constraints (HTMT = 0.87), slightly exceeded this level. This finding is not unexpected, as regulatory processes in the construction sector can influence perceptions of ease of use, creating a theoretically meaningful overlap. Importantly, the HTMT value remains well below the widely accepted 0.90 threshold recommended for social sciences and exploratory research (Hair et al., 2021). Therefore, discriminant validity is still supported. All other construct pairs fell comfortably below both thresholds, further confirming adequate discriminant validity. Finally, while VIFs confirm that collinearity does not bias the structural estimates, potential endogeneity due to unobserved variables cannot be entirely ruled out. Nevertheless, the combination of low structural VIFs and theoretical justification supports the reliability of the estimated relationships.

The structural model was evaluated using standard PLS-SEM procedures, assessing collinearity, R², adjusted R², Q² via blindfolding, f² and SRMR. Path significance was tested using bootstrapping with 5,000 subsamples and 95% bias-corrected intervals. Control variables such as region, firm size and experience, showed no significant effects, and all hypothesized paths remained stable. T-values and p-values in Table 6 show that perceived ease of use and enhanced management significantly increase intentions to adopt emerging technologies (p < 0.05). Conversely, relative advantages, financial incentives and competitive pressure were not significant predictors, indicating these factors did not influence adoption intentions in this sample.

Training and skill development is seen as key to accelerating technology adoption by 40% of respondents, followed by financial incentives (23%), partnerships with technology suppliers (20%) and clearer regulations (17%). Nearly half (47%) use other technologies, including generative AI (47%), face recognition (16%), IoT (13%) and virtual reality/drones (12%), primarily for data collection, reporting and performance monitoring. Usage frequency is high, with 50% using these technologies daily, 37% several times a week and all respondents using them at least monthly, highlighting widespread engagement and integration in professional activities.

This study explores construction managers’ knowledge and proficiency in emerging technologies, including AI, RPA, cloud computing and data analytics, identifying adoption barriers and success factors. Results show RPA has the lowest knowledge and experience (40%), indicating limited exposure. AI and cloud computing demonstrate moderate knowledge and experience (60%), while data analytics exhibits the highest knowledge (80%) but lower hands-on experience (60%), highlighting a theory–practice gap. Mean rank comparisons reveal that managers generally understand concepts better than they can apply them, and standard deviations indicate variation in skills across respondents. These findings align with literature emphasizing growing data-driven approaches in construction, such as BIM and digital dashboards (Marzouk and Enaba, 2019). Self-reported responses may be biased, yet the results suggest that emerging technologies remain complex and managers often lack the technical expertise and practical skills necessary for full implementation, underscoring the need for targeted training and skill development.

This study identifies key obstacles shaping the adoption of emerging technologies in construction. The primary barrier is lack of expertise, followed by limited funding, insufficient awareness and resistance to change, highlighting the need to enhance digital proficiency and overcome organizational inertia. Training and skill development are critical, with structured programs, workshops and hands-on experience essential to bridging knowledge gaps. Financial support, such as grants, subsidies or tax incentives, was noted by 23% of respondents as a facilitator, reflecting resource constraints (Nnaji and Karakhan, 2020). Collaboration with technology providers also supports adoption by offering technical expertise, tailored solutions and knowledge transfer, helping reduce resistance and increasing awareness. Additionally, the absence of clear regulatory guidelines can discourage adoption, emphasizing the importance of standards, compliance frameworks and best practices. Overall, addressing both human and structural barriers is crucial to advancing the effective implementation of emerging technologies in the construction sector.

This study investigates the impact of emerging technologies on managers and the construction industry, as well as factors influencing adoption intentions. AI is perceived as the most transformative, with 26% of respondents indicating a high impact and 37% a large impact on managers and 28% and 42%, respectively, anticipating significant sectoral effects, highlighting its pivotal role in project management, automation and decision-making. RPA shows a smaller but notable influence, with 25% recognizing a significant effect on managers and 26% on the industry, though 16% and 11% reported no impact. Cloud computing is widely seen as an essential enabler of digital transformation, supporting collaboration, data storage and project management, with only 8% reporting no managerial impact and 6% no sectoral impact, while 30% and 26% noted significant effects. Data analytics is identified as critical for managerial effectiveness, with 43% and 44% acknowledging substantial impacts on managers and the sector, emphasizing predictive analytics and data-driven decision-making. Overall, AI and data analytics are perceived as the most influential, followed by cloud computing and RPA, underscoring the need for targeted training, strategic investment and organizational readiness to maximize adoption benefits.

Accordingly, the results indicate that AI and data analytics are likely to exert the greatest influence on future managers, followed by cloud computing and RPA. A similar pattern is observed regarding the projected impact of these technologies on the construction sector. These findings underscore the imperative for future managers to develop competencies in data analysis, AI-driven decision-making and cloud-based solutions to remain competitive in an increasingly technology-driven environment. Moreover, the results highlight the strategic importance for the construction industry to prioritize investments in AI, data analytics and cloud computing to enhance operational efficiency, innovation and long-term sectoral resilience.

The analysis of factors influencing individuals’ intentions to adopt emerging technologies can be grouped into three main categories: technological factors, organizational factors and environmental factors. Technological factors include constructs such as relative advantages and ease of use. Prior research indicates that ease of use has a major impact on technology adoption intentions, as individuals are generally more inclined to adopt technologies that are simple to use (Na et al., 2022; Sepasgozar and Davis, 2018). These findings are consistent with the current study, which also complements earlier results highlighting complexity as a major barrier to cloud computing adoption (Gutierrez et al., 2015). Therefore, simplifying processes and enhancing usability increases the likelihood of successful technology adoption.

In addition, emerging technologies are generally expected to provide greater advantages and value compared to existing solutions (Park et al., 2024; Sepasgozar et al., 2023). Although prior studies frequently recognize relative advantages as a significant determinant of adoption (Aghimien et al., 2022), the present study finds that it does not significantly influence managers’ intentions to adopt emerging technologies in the construction sector. This may be explained by users’ pre-existing awareness of the benefits, rendering relative advantages a non-differentiating factor. Moreover, relative advantages may exert an indirect influence, consistent with the view that it may not be a critical determinant within comprehensive decision-making frameworks (Na et al., 2022; Sepasgozar and Davis, 2018). Within organizational factors, the commitment of top management emerges as a key driver of adoption. Respondents indicate that fostering adoption requires strong management support, which appears to outweigh even ease of use as a predictor of innovative technology adoption. Existing literature consistently supports the positive impact of top management commitment on technology adoption (Gutierrez et al., 2015; Na et al., 2022; Naji et al., 2024; Sepasgozar and Davis, 2018; Yigitbasioglu, 2015), emphasizing that strong leadership not only facilitates rapid adoption but also shapes long-term organizational capabilities (Wernicke et al., 2023). However, financial incentives were not found to have a significant impact on the intentions to adopt emerging technologies. This suggests that while subsidies or cost-related benefits may provide some support, they are not the primary drivers of adoption decisions. Environmental factors also influence adoption decisions. Prior studies suggest that companies facing intense competition are more likely to adopt new technologies, as competitive pressure can strongly shape adoption behavior (Aghimien et al., 2021; Ameyaw et al., 2023; Lekan et al., 2021). Interestingly, the current study finds that competitive pressure does not significantly affect the adoption of emerging technologies. This may be due to the low diffusion rates of developing technologies or the possibility that firms in the sample do not yet experience substantial competitive pressure. Similarly, regulatory constraints did not exert a significant influence on the intentions to adopt emerging technologies. A possible explanation is that in developing countries, regulatory frameworks are often less stringent, inconsistently enforced or still evolving, which reduces their effectiveness as a motivating factor for adoption. From a strategic perspective, organizations may prioritize internal drivers over external pressures. Nevertheless, previous literature highlights competitive pressure as an influential factor in adoption decisions, indicating a contextual divergence (Wernicke et al., 2023).

This study provides a comprehensive examination of managers’ knowledge, practical experience and adoption intentions regarding AI, RPA, cloud computing and data analytics in the construction industries of developing countries. By applying the TOE framework and analyzing survey data through SEM, the research identifies the most influential factors shaping technology adoption. The key findings and their importance are summarized below:

• Data analytics (68%) and AI (63%) emerge as the most transformative technologies for managerial practices and sector operations, followed by cloud computing (53%) and RPA (46%), indicating a clear hierarchy of perceived influence.

• Managers’ theoretical knowledge surpasses their practical experience, revealing a significant awareness–application gap that must be addressed to unlock the full potential of emerging technologies.

• Ease of use (β = 0.331, p < 0.05) and strong top management commitment (β = 0.501, p < 0.05) emerge as the strongest drivers of adoption, while competitive pressure, financial incentives and relative advantages show non-significant effects. Regulatory constraints also demonstrate a statistically significant yet comparatively moderate influence (β = 0.298, p < 0.05), indicating that adoption in developing countries remains primarily shaped by internal organizational factors rather than external pressures.

• Organizational barriers, including low awareness, limited financial resources, lack of hands-on experience and resistance to change, hinder adoption; these can be mitigated through structured training, skills development, financial incentives and targeted awareness campaigns.

• The study extends the TOE framework by empirically demonstrating that in resource-constrained contexts, organizational and technological factors outweigh environmental pressures in shaping adoption decisions.

• Future research could use longitudinal panels, multi-country comparative studies, AI pilot case studies or refined measurement instruments to further validate and extend the findings.

This study provides several managerial and policy insights for advancing Construction 4.0 in developing countries. Firms should prioritize investments in AI, data analytics and cloud computing to improve efficiency, innovation and resilience. Such technologies can enhance coordination, reduce project delays and foster innovation across the construction value chain, contributing to improved project delivery and competitiveness. Collaboration between construction firms, technology providers and academic institutions is essential to accelerate knowledge transfer and capability development. Such partnerships can help firms overcome skills gaps, adapt digital tools to local conditions and mitigate resistance to change. At the policy level, governments and industry associations should support adoption through tax incentives, targeted grants and digital capacity-building programs. These initiatives can enhance awareness, promote standardized digital practices and strengthen the construction sector’s readiness for technological transformation. Furthermore, educational programs and hands-on workshops are critical to develop managerial and technical competencies required for Construction 4.0. These initiatives would enable a more sustainable and inclusive transition toward the digitalization of construction processes.

To the best of our knowledge, this is the first study to explore the use of emerging technologies from the standpoint of managers in developing countries through the TOE framework. While it does not claim to capture all factors influencing organizational adoption of emerging technology, the findings provide a foundation for region-specific strategies promoting sustainable digital transformation.

There are several restrictions on this study. The results may not be as broadly applicable as they could be because the sample is limited to 15 nations. Consequently, care should be taken while interpreting the findings. By investigating whether the adoption and influencing variables of new technologies highlighted here are consistent across a wider range of developing nations, future study could build on this work. The survey’s scope, which only asked participants to identify the technologies in use and evaluate the effects of four technologies on the accounting industry, is another drawback. Richer insights would come from a more thorough examination of which engineering processes, functions or modules are impacted, and how these technologies are used differently across businesses. Alternative research methods, especially in-depth case studies, might be needed to answer such problems. Additional limitations include potential sampling bias, as participants were primarily recruited via LinkedIn and snowball sampling, which may not fully represent the population of managers in the targeted industries. The study relies on self-reported data, introducing the possibility of response bias. Common method bias (CMB) may also affect the observed relationships due to the single-source survey design. Moreover, non-response bias could influence the generalizability of the findings.