Purpose This research aims to propose and validate a methodology for customizing generative artificial intelligence (GenAI) to enhance its performance in a specialized domain in construction, using Finnish hospital construction as a case study. Design/methodology/approach Using a design science methodology, the study follows a three-step process: (1) problem and requirement framing, (2) solution development and (3) validation. The study applies specific research methods for GenAI customization to operationalize this framework, including domain-specific dataset curation, retrieval-augmented generation and iterative, expert-in-the-loop, qualitative validation. Findings The study indicates that a cost-effective GenAI customization can provide more relevant and useful responses than standard models in domain-specific use with approximately 120 work hours invested in customization. The validated methodology provides a transferable framework that encompasses four key stages: (1) problem and requirement framing; (2) domain-specific dataset creation, curation and sanitization; (3) model constitution and retrieval-augmented configuration; and (4) expert validation and refinement. The most resource-intensive stages were creating and curating the dataset and recruiting experts for validation. Originality/value This study offers an original contribution by demonstrating customizing GenAI for the specific localized context in construction. It underscores the importance of curated data sets and provides a validated pathway for continuous data-driven improvement.
Purpose Existing reviews on modular construction are largely dominated by building- or system-level perspectives, while the limited component-level reviews remain mainly connection-focused. This leaves a limited understanding of what modular building components have been studied, what design methods are used at the component level and what key challenges shape future research. This study aims to address these gaps through a systematic literature review of modular building component design. Design/methodology/approach A systematic literature review was conducted following PRISMA guidelines. Scopus and Web of Science were used as the primary databases, and 75 peer-reviewed journal articles published between 2010 and 2024 were selected after screening and eligibility assessment. Template analysis was applied to synthesise evidence on component types, design methods and approaches, and reported challenges and future directions. Findings The review identifies nine component types and organises design methods into three functional roles: design generation, design strategy and decision-making, and design verification and improvement. It also highlights four recurring challenges: uneven component coverage, limited transferability of verification evidence, weak upstream integration of life cycle constraints and the limited integration of AI and digital methods with validated evidence. Originality/value This review provides a component-centred and workflow-oriented synthesis of modular building component design, extending prior building-level and connection-focused reviews. By integrating component types, material categories and design methods within one analytical framework, it clarifies how current knowledge is distributed across generation, decision strategy and verification. It also informs implications for design practice, industry implementation, education and policy, supporting more reusable, life cycle-informed and performance-grounded design workflows.
PurposeLow profit margins, high turnover and heavily transaction-oriented nature of the construction industry hinder the opportunities for innovations in low-carbon mechanisms. The complex and lengthy supply chains created by the involvement of multiple stakeholders make carbon emissions tracking complicated. The construction industry lacks a mature carbon ecosystem, even if there are inevitable inherent carbon emissions in material use that require carbon offset. Meanwhile, carbon trading allows such offsets, yet its adoption is markedly limited in the construction industry. In contrast, other industries engage in advanced carbon trading practices, supported by technologies such as blockchain, enabling greater transparency, efficiency and governance. This study aims to examine the potential of blockchain technology in enhancing carbon trading practices in the construction industry through cross-sectoral insights. Design/methodology/approachUsing a preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a total of 260 publications were examined to extract quantitative trends, while 72 were reviewed for thematic insights. FindingsThe findings revealed that blockchain-integrated carbon trading research is a growing research domain. Several industries have researched improving transparency, efficiency and governance of carbon trading through blockchain integrations. A framework is developed to enhance carbon trading practices in construction, offering practical pathways for strengthening its carbon ecosystem and accelerating decarbonisation goals. Originality/valueThis study bridges the gap in existing theories on how cross-sectoral insights can be used to enhance carbon trading in construction. This study holds practical significance for the construction industry, since blockchain-enabled carbon trading offers data-driven solutions to accelerate the transition to a decarbonised construction industry.
Purpose The purpose of this study is to examine the complex challenges to sustainability in the built environment and uncover their interlinked causal relationships. Design/methodology/approach This study applies grey influence network analysis (GINA) and its novel interpretative extension (iGINA) to model, quantify and prioritise the interrelationships among barriers using expert judgements. Findings The iGINA analysis revealed misaligned stakeholder incentives as the most influential barrier, followed by high initial cost, while technological readiness ranked lowest, challenging tech-centric assumptions. Research limitations/implications Expert-driven data limits generalisability, but advances grey systems theory for uncertain, large-scale networks. Practical implications This study prioritises realigning incentives via performance-based contracts, green bonds, life cycle costing and regulatory sandboxes − breaking economic barriers for scalable green adoption. Originality/value iGINA uncovers feedback loops and leverage points overlooked by traditional methods, offering a blueprint for managers, policymakers and researchers.
Purpose Conventional life cycle assessments (LCAs) of cement production typically provide static embodied carbon inventories that offer limited insight into how emissions respond to changes in key process parameters. This limitation is particularly acute in data-scarce regions, where reliance on generic databases can misrepresent both emission intensities and mitigation priorities. This study aims to develop and validate a plant-level, predictive framework for embodied carbon assessment in cement production, and to identify the dominant process drivers of embodied carbon at the plant level. Design/methodology/approach A hybrid framework integrating process-based LCA with targeted statistical modelling is developed under cradle-to-gate system boundaries (A1-A3). Primary plant-level data were obtained through a structured questionnaire-based survey of a cement plant in Qatar, covering five operational years (2020-2024). The approach distinguishes between chemistry-driven calcination emissions, modelled using multiple linear regression performed in IBM SPSS Statistics (v.25), and deterministic activity-based components, represented through governing equations. A one-at-a-time (+/- 10%) sensitivity analysis was used to rank the influence of key parameters. The framework is applied to, and validated against, primary operational data from a cement plant in Qatar. Findings The framework yields an embodied carbon intensity of 652 kg CO(2)e per tonne of Ordinary Portland cement (OPC) for the case-study plant (cradle-to-gate, A1-A3), with calcination dominating (81%), followed by fuel combustion (12%) and electricity (5%). Sensitivity analysis confirms the clinker-to-cement ratio and CaO content as the strongest decarbonisation levers: a 10% clinker-ratio reduction yields approximately 62 kg CO(2)e per tonne. Statistical models (R & sup2; > 0.9) validate the predictive equations and confirm the framework's ability to generate decision-ready emission curves for clinker substitution, fuel switching and grid decarbonisation scenarios. The Qatar benchmark sits in the lower-mid range of international OPC values, attributable to natural-gas firing and moderate electricity intensity. To the authors' knowledge this is the first published plant-level OPC embodied carbon value for Qatar and the wider Gulf Cooperation Council region. Originality/value The study advances cement embodied carbon assessment by transforming static LCA inventories into predictive, scenario-ready relationships using a plant-level hybrid framework. The methodology is transferable to other data-scarce cement production contexts and provides a decision-oriented basis for embodied carbon benchmarking and decarbonisation policy development.
PurposeRecent technological advancements, also known as Industry 4.0, impact construction processes and, thus, the way people work. Previous research claims that despite extensive research, the implications for people are often overlooked, and the dynamics within an organisation when technology is introduced are widely ignored. This study/paper aimed to develop a conceptual technology acceptance and adoption framework founded on contingent authority innovation adoption theory, the technology organisation environment (TOE) framework and the technology acceptance model (TAM). Design/methodology/approachWithin the Scopus database, 193 journal publications (in English) were systematically analysed. The systematic literature review was conducted in February 2024, following PRISMA guidelines. The selected articles were content analysed to identify themes, allowing for a robust conceptual framework development. FindingsThe analysis identified 12 factors influencing the management’s intention. Under secondary adoption, 20 factors influenced the perceived ease of use, and 17 factors affected the perceived usefulness. Originality/valueThe study presents insights into the acceptance and adoption of technology from an organisational perspective. It provides a comprehensive review of Industry 4.0 acceptance and adoption in the CI, leading to the development of the conceptual framework.
Purpose Establishing net-zero carbon emissions requirements can provide a meaningful reference for construction clients to choose the most qualified contractors in bid evaluation that will not only meet the client’s specifications but also meet the global net-zero emission target. Moreover, integrating net-zero emissions requirements into contractor selection criteria in bid evaluation will stimulate the bidders to improve their construction schemes, enabling the construction phase to be environmentally friendly. This study aims to develop a novel conceptual framework for net-zero emission contractor selection. Design/methodology/approach The traditional method of conducting a systematic literature review based on preferred reporting items for systematic review and meta-analyses protocol was used in this study. The methodology is grounded on an explicit and structured procedure for searching, selecting and reviewing relevant literature in a subject matter. Findings The paper identifies 40 net-zero carbon emission requirements and classified them into seven bid evaluation categories. The study further presents, a novel conceptual framework that provides a methodical and holistic approach to selecting contractors who are equipped to meet net-zero emissions target in construction projects. Originality/value The novelty of this study stems from the development of an innovative conceptual framework to establish a structured and holistic model that evaluates contractors not just based on the traditional contractor selection criteria but also on the capacity to effectively and efficiently meet net-zero emissions goal in construction projects. This is imperative as there is an exigence for the construction industry to drastically cut down on carbon dioxide emissions generated by its activities.
PurposePrefabricated construction is a transformative approach to reshaping the construction industry, enhancing productivity and efficiency. The use of robotics has emerged as a critical tool in advancing this practice; therefore, assessing its productivity has become prudent. This study aims to examine the productivity landscape of assembly robots, aiming to provide a comprehensive classification of their types, identify the key elements of their cycle time and determine the factors that influence their overall productivity. Design/methodology/approachA scientometric analysis and systematic literature review were used to reveal the research trend and provide in-depth insights into the subject matter. A total of 72 papers selected using the PRISMA guidelines underpinned the results of this study. FindingsThe results revealed four predominant types of assembly robots; each uniquely suited to specific tasks. Transport, alignment, installation and return were recognised as significant cycle time elements, with additional tasks varying according to robot function. The productivity of robots for assembly was found to be influenced by robot design and operation, planning and management strategies, as well as communication and feedback systems. Originality/valueWith the growing age of digitalisation and improved construction practices, this study uniquely offers insights for optimising the deployment of semi-autonomous assembly robots in prefabricated construction and identifies avenues for future research.
Purpose Technological obsolescence and weak construction-related institutional frameworks within Zimbabwe’s construction sector underscore the need to develop and implement a sustainable construction model that explicitly integrates technical sustainability. This study aims to address this knowledge gap by developing and validating a stochastic sustainable construction model by delineating a fourth pillar of “technical”, to the three existing pillars of sustainability (namely, social, environmental and economic). Design/methodology/approach Model development was premised upon complexity and sustainability theories. Primary data on the relevance of sustainability indicators across the four pillars was collected through an online questionnaire survey completed by construction professionals from consultancy firms, construction companies, government bodies and academic institutions. Construct validity was demonstrated using convergent and discriminant validity. Findings Results revealed that technical sustainability (which included: policy support for decent working conditions; implementation of efficient technological advances; adequate sustainable construction practices; and adequate construction project technical management) constitutes a fourth pillar of sustainability for achieving sustainable construction. Cumulatively, the findings confirm the utility of complexity theory for interrogating sustainable construction. A major recommendation made is to allocate incentives for realising green technology and modern construction methods adoption. While the study’s findings are limited by content and context, the approach can be replicated in various countries. Originality/value This study validates technical sustainability as a fourth pillar for achieving sustainable construction in a developing country like Zimbabwe. In addition, the study exposes the contribution of sustainable indicators to the various pillars of sustainability.
Purpose Typically, the number of workers per team is determined based on experience. This study aims to propose an integrated intelligent model aimed at reducing lost productivity and optimizing team sizes in construction projects to align with stakeholder interests and project objectives. The model focuses on maximizing profit and minimizing loss under the current project conditions and available resources. Subsequently, the model provides reliable estimates of the necessary information and the impacts of worker arrangement selection on team performance for clearer decision-making by managers. Design/methodology/approach This study extracts stakeholder criteria weights using the best-worst method. The outputs are integrated with TOPSIS calculations to determine the optimal number of workers per group based on productivity and project needs. Furthermore, a hybrid metaheuristic algorithm combined with artificial neural networks is optimized to accurately estimate the impact of managerial decisions. Findings A case study was conducted using data from a construction project. Results significantly demonstrated the impact of project goal orientation and stakeholder criteria on prioritizing workforce sizes for achieving maximum productivity. The intelligent model adapts to changing project conditions. In addition, an optimal combination of candidate metaheuristics with neural networks was achieved for accurately estimating the impacts of worker arrangements. Practical implications This dynamic multi-objective model facilitates optimal productivity aligned with stakeholder goals and current project conditions, providing informed insights for managers. Originality/value This model presents an intelligent, knowledge-based approach addressing the unique and dynamic environments of projects to enhance workforce productivity while offering reliable forecasts applicable across diverse construction activities.
Purpose Digital transformation in construction supply chains requires the coordinated development of dynamic capabilities. While prior research identifies digital sensing, seizing and transforming as critical enablers of strategic renewal, limited attention has been paid to how these capabilities are perceived and aligned across hierarchical levels. This study aims to examine how dynamic capabilities for digital transformation are interpreted, prioritised and linked by managers, digital change agents and operational staff in the construction industry. Design/methodology/approach Digital dynamic capabilities were categorised into sensing, seizing and transforming dimensions. Perceptual data were collected from 74 respondents across three hierarchical groups within a construction firm operating in German and Austrian markets. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was applied to model perceived interdependencies and cause–effect relationships between capabilities. Statistical tests were conducted to analyse differences in perceived capability manifestations within and across hierarchical levels. Findings The analysis shows that substantial perceptual divergence exists across hierarchical levels. Senior managers frame digital capabilities primarily as strategic enablers of competitiveness and long-term positioning, digital change agents adopt an intermediary implementation-oriented perspective and operational staff evaluate capabilities through their immediate impact on workflows and routines. Originality/value This study advances dynamic capability research by empirically modelling non-linear interdependencies between digital capabilities in a construction context. It extends micro foundational perspectives by demonstrating how hierarchical position shapes cognitive construction and prioritisation of dynamic capabilities. Methodologically, the study introduces DEMATEL as a systematic approach to modelling perceived causal structures in construction management research.
PurposeIn recent years, research in construction robotics has significantly advanced within the construction sector. However, many collaborative robotic systems have yet to be effectively integrated into large-scale construction settings due to limitations such as restricted workspace, deployment complexity and insufficient adaptability to dynamic on-site conditions. To address this issue, this paper aims to establish an augmented reality (AR)-assisted human-robot collaboration (HRC) workflow that uses low-fidelity visual markers to spatially register a modular robotic unit with a digital wall model, while enabling human-guided robot relocation supported by real-time feasibility visualisation.Design/methodology/approachThe authors evaluate the complete cyber-physical workflow by constructing two large-scale masonry-like walls, each spanning 3 m, exceeding the operational range of the collaborative robotic arm and requiring repeated manual relocation of the modular robotic unit. After assembly, both structures were 3D-scanned and compared with their 3D digital models using cloud-to-cloud (C2C) deviation analysis. Segment-level deviation was used to assess the reliability of robotic pick-and-place execution, while global deviation quantified spatial drift introduced by the AR-based registration during repeated relocation cycles.FindingsCluster deviation analysis confirms consistent robotic pick-and-place accuracy across repeated assembly cycles, demonstrating reliable task-level execution. However, global deviation results reveal accumulated spatial drift caused by AR-based registration, indicating that the proposed workflow is most suitable for construction scenarios requiring moderate accuracy ( +/- 5 mm tolerance) rather than high-precision industrial fabrication.Research limitations/implicationsThe study's accuracy was limited by the use of only two visual markers, increasing susceptibility to AR spatial drift during repeated registration. The geometric simplicity of the dry-stacked walls restricted evaluation of more complex construction scenarios, and the controlled laboratory lighting conditions did not reflect the variability of real construction environments. In addition, the robotic arm's operational range was constrained by the geometry of the mobile platform. Future work should investigate the use of additional markers or sensor-fusion methods, evaluate performance in real on-site conditions, and explore improved mobile platform designs to extend reachability and robustness.Practical implicationsThe study demonstrates that AR-assisted spatial anchoring can improve the accessibility of robotic bricklaying in construction by reducing reliance on complex simultaneous localisation and mapping-based localisation and costly autonomous platforms. The proposed workflow allows operators to manually reposition modular robotic units using intuitive AR feedback, enabling reliable assembly without advanced robotics expertise. This approach lowers implementation complexity, reduces training requirements and supports wider adoption of collaborative robotic systems in small-to-medium construction projects.Social implicationsThe study supports a more human-centred model of construction automation by positioning robots as collaborative tools rather than replacements for labour. By simplifying robot operation through AR guidance, the proposed workflow can reduce workforce resistance often associated with job displacement and technological complexity. The system allows operators to retain users' agency through manual robot repositioning while benefiting from robotic precision during execution. This approach may facilitate workforce upskilling, reduce physical strain and improve safety, while broadening access to robotic technologies across diverse construction teams, thereby supporting more inclusive and socially sustainable adoption of automation in the construction sector.Originality/valueThis study contributes (1) a validated human-in-the-loop workflow for large-scale robotic bricklaying that combines human-guided robot relocation with robot-executed geometric placement, (2) an AR-based spatial anchoring method using low-fidelity visual markers to support repeated robot re-registration during large-scale assembly, and (3) an AR-supported inverse-kinematics feasibility visualisation that enables informed relocation decisions across repeated construction cycles.
Purpose Modern Methods of Construction (MMC) represent a potential pathway towards addressing Australia's long-lasting housing undersupply and construction productivity challenges. Yet MMC constitutes not merely a technological change but a socio-technical transition that reconfigures workforce practices and knowledge systems. This study aims to examine how built environment workforce dynamics co-evolve with MMC adoption through the identification and resolution of systemic contradictions.Design/methodology/approach This research adopted a multi-method qualitative methodology combining semi-structured interviews and fieldwork. Twenty interviews were conducted with participants across diverse MMC company profiles, including designers, manufacturers and builders. Fieldwork using an observational-interview hybrid method was undertaken at 15 companies across five Australian states. Data were analysed using Framework Analysis, with Expansive Learning Theory providing the analytical lens for interpreting contradictions within activity systems.Findings The analysis identifies eight interconnected contradiction themes driving workforce transformation, including temporal resequencing of decision-making, coordination locus shift, professional knowledge boundary tensions and education system-industry pace mismatch. These contradictions do not operate in isolation but cascade across domains - temporal resequencing exposes knowledge limitations, generating coordination distortions that reveal education misalignment. This cascading dynamic reshapes workforce requirements differently across design/engineering, manufacturing and assembly.Originality/value This study demonstrates how contradictions function as interconnected drivers of workforce transformation rather than discrete problems. The findings extend taxonomic approaches that conceptualise skills as specifiable occupational attributes by revealing skills as emergent properties - arising when workers, technologies and organisational arrangements encounter incompatible demands. The integration of Expansive Learning Theory with socio-technical transitions theory positions workforce transformation as integral to MMC transition rather than a downstream consequence.
Purpose Selecting sustainable construction materials can help reduce the environmental burden of the construction industry. This study aims to assess the comparative environmental and financial performance of strand-woven bamboo (SWB) flooring as an alternative to conventional construction flooring options primarily used in Indian households, namely ceramic, marble and Kota stone. The temporary carbon sequestration potential of bamboo flooring is also estimated. Design/methodology/approach This research uses life cycle assessment (LCA) to quantify environmental burdens across nine midpoint and three endpoint impact categories for the flooring options. Primary data for bamboo floors on materials, costs and energy is collected from an industrial bamboo products manufacturer in India, while the secondary data is fetched from the literature and the Ecoinvent 3.9.1 database. Flooring installation financial data was collected from a building constructor in India for all compared variants. Findings The study found that bamboo flooring showed the least impact among other compared options in all midpoint and endpoint impact categories, except for surplus ore potential. The manufacturing phase of the bamboo floor contributes predominantly to the environmental impacts, while electricity consumption is the most significant resource influencing the overall results. The carbon sequestration of Indian bamboo products is lower with the current allocations in this method. However, the financial analysis indicates that bamboo flooring installation is more expensive than the other three options. Originality/value This study proposes a suitable alternative to conventional flooring materials commonly used in building construction in India. The study is unique in that it compares the environmental and financial performance of bamboo flooring with three prominent alternatives used in India and calculates the carbon sequestration of bamboo flooring products throughout their life cycle. This study can guide policy formation and sustainability planning for the decarbonization of the construction industry.
Purpose This paper aims to propose a framework for the digitalization and improvement of design operations in offsite construction (OSC). It addresses the limitations of traditional methods by introducing custom generative tools that automate modular layout design based on optimal solutions for OSC. Design/methodology/approach This study applies Design Science Research to develop the framework, integrating principles of OSC, generative design and digitalization to guide the automation and optimization of modular layouts. In addition, an empirical implementation was conducted within the design office of a case study company to demonstrate its applicability. Findings The implementation of the framework led to significant improvements in the company’s design process for temporary modular changing rooms. Quantitative data revealed a substantial reduction in the time required for layout design, ranging from 58% to 72%. User feedback was collected through a survey administered to the design teams of both the case study and control company, yielding an overall positive response. The findings indicate high levels of agreement across all evaluated criteria, including efficiency, feasibility, sufficiency and applicability. Originality/value Current research on generative design applications in OSC remains largely experimental, with limited practical application and validation. This study presents a generative framework for modular layout design, including detailed steps and replicable methods, along with its implementation for changing facilities within an OSC company, where measurable improvements in layout generation time were achieved.
Purpose This study aims to examine how the causes of ineffective building information modeling (BIM) execution plans (BEPs) influence the strategies required to improve BEP development. Design/methodology/approach A systematic literature review of 57 articles and semistructured interviews with 20 BIM professionals identified 30 causes of ineffective BEPs and 32 improvement strategies. A total of 122 BIM professionals evaluated the criticality of the causes and strategies through a questionnaire survey. The collected data were analyzed using exploratory factor analysis (EFA) and partial least squares structural equation modeling (PLS-SEM). Findings EFA grouped the causes into two constructs, namely, BEP deficiencies and BEP development issues, and the strategies into four constructs: adaptability and continuous update, BEP management requirement, comprehensive and integrated BEP and BEP technical aspects and deliverables. The PLS-SEM results show that BEP development issues significantly influence all four strategy constructs, whereas BEP deficiencies significantly influence only adaptability and continuous update. These findings indicate that development-stage problems require broader strategic responses than deficiencies identified in completed BEPs. Originality/value This study extends existing BEP research by modeling the relationship between the causes of ineffective BEPs and strategies required to improve BEP development. The findings provide a basis for strengthening BEP development in BIM-based construction projects.
Purpose Intelligent construction technology is a key driver for enhancing the efficiency and quality of engineering project delivery. However, its practical application exhibits significant fragmentation and uneven adoption. This study aims to systematically investigate the driving mechanisms of intelligent construction technology in engineering projects.Design/methodology/approach To systematically address this problem, this study develops a moderated mediation model based on the Technology-Organization-Environment (TOE) framework, aiming to uncover the pathways through which technological, organizational and environmental dimensions influence the application of intelligent construction technology, while examining the mediating role of project management effectiveness and the moderating effect of project complexity. Through a review of literature, a theoretical model of influencing factors was constructed, and structural equation model was applied to analyze data from 246 questionnaires completed by Chinese engineering construction practitioners.Findings The results indicate that technological, organizational and environmental dimensions all exert significant positive effects on the application of intelligent construction technology, with the technological dimension playing the most prominent role. Project management effectiveness partially mediates the relationship between these three dimensions and the application of intelligent construction technology. Furthermore, project complexity negatively moderates the influence of both technological and organizational dimensions on project management effectiveness, while the environmental dimension demonstrates relatively strong robustness against complexity.Originality/value This study not only provides an integrated perspective for understanding the application mechanisms of intelligent construction technology but also offers theoretical and practical insights for project managers to develop differentiated implementation strategies under varying contexts of project complexity.
Purpose This study aims to develop a decision-making approach that supports sustainable building upgrades or modifications where choices include a set of climate change risk mitigation options. The approach places all costs and benefits, both direct and intangible, onto a common monetary scale that includes ripple effects of potential climate-induced disruptions. Design/methodology/approach Current approaches do not include rigorous methodologies that can be implemented by practitioners, and they do not monetize disruption ripple effects. Practitioner approaches often misrepresent actual conditions because they tend to rely on ordinal transformations that have been shown to be inaccurate and abstract to decision makers. The methodology employs pairwise comparisons and willingness to pay to quantify the direct and ripple impacts of a specified climate event, with uncertainty expressed using color-coded visualizations. Findings A prototype decision support system was created using R-Shiny. It was applied to a building under consideration for upgrades that may be affected by future climate change impacts. Operating the decision support tool requires a stakeholder team to compare pairs of risk response options based on their financial consequences that account for their costs and effectiveness, including the ripple effects of climate-induced disruptions. Research limitations/implications A prototype decision support system was created using R-Shiny, and applied to a building under consideration for climate-related upgrades. A stakeholder team used the decision support tool to compare pairs of risk response options based on their effectiveness and monetary consequences, including the ripple effects of climate-induced disruptions. Results showed the method to be robust in the presence of moderate judgment inconsistencies. Originality/value The proposed methodology addresses research gaps by presenting an intuitive approach for stakeholder teams to rank risk response options while explicitly accounting for ripple effects by translating them into monetary terms. It can be used by public sector managers to prioritize building upgrade projects and by policy makers to create building regulations for facilities embedded in interconnected systems.
Purpose Generative artificial intelligence (GenAI) holds significant potential to improve accuracy, reduce uncertainties and support proactive financial decision-making throughout the project lifecycle. Despite these promising capabilities, the adoption of generative AI in construction cost management remains limited and uneven. This study, therefore, aims to examine the barriers to adopting GenAI for cost management in the Nigerian construction industry and to propose viable strategies to overcome them. Design/methodology/approach A quantitative research approach was adopted, with data collected through structured, closed-ended questionnaires administered to construction professionals. The data were analysed using both descriptive and inferential statistical techniques. Findings The study identified nine critical barriers to the adoption of GenAI for cost management in the Nigerian construction industry. Exploratory factor analysis grouped these barriers into two principal components: internal organisational constraints and external risk and environmental uncertainty factors. The results highlight that GenAI adoption is a socio-technical process influenced by both organisational readiness and external conditions. In addition, the study provides practical value by developing a structured matrix that aligns each barrier with targeted strategies across technology, people, process and risk management dimensions, offering actionable guidance for improving adoption. Originality/value This study contributes to digital transformation in construction by providing empirical, context-specific evidence on the adoption of GenAI for cost management in the Nigerian construction industry. It identifies and categorises nine critical barriers into internal organisational constraints and external risk and environmental uncertainty factors. The study further develops a structured matrix that links these barriers to targeted strategies across the technology, people, process and risk management dimensions. In addition, it extends the technology acceptance model by demonstrating how organisational and environmental factors influence GenAI adoption in a complex context.
Purpose Sustainability research on modern methods of construction (MMC) has predominantly conceptualised sustainability through assessed performance outcomes, particularly environmental indicators. While this literature demonstrates the sustainability potential of MMC, it offers limited explanation of how sustainability-oriented value is realised during project delivery. This study aims to develop a project-level conceptual framework to explain how environmental, social and governance (ESG)-centred project value is realised in MMC projects. Design/methodology/approach This study adopts a qualitative conceptual research approach focused on framework development. Insights from project value research, delivery-oriented perspectives and project capability literature are integrated with existing MMC sustainability research to inform the development of a project-level conceptual framework. Findings Existing MMC sustainability research is dominated by an outcome-oriented focus with limited attention to project delivery execution and implementation conditions. In response, this study develops an ESG-centred capability-focused conceptual framework that conceptualises sustainability as ESG-centred project value realised through ESG-centred project delivery execution across the MMC project life cycle and enabled by ESG-centred project team capability. The framework provides a project-level explanation of how ESG-centred project value is enacted in practice. Research limitations/implications As a conceptual study, the proposed framework has not yet been empirically tested. Practical implications The framework highlights the importance of embedding ESG priorities within project delivery execution and project team capability, rather than relying on technological adoption alone. Originality/value By shifting the analytical focus from performance measurement to value realisation through project delivery, the framework offers a novel project-level explanation of sustainability implementation in MMC.
