Purpose The infrastructure sector has long suffered from challenges in bringing innovations into practice. This study aims to investigate how adapting implementation science and takt production principles could improve the innovation implementation capabilities of organizations in the sector. Design/methodology/approach This research belongs to the field of innovation implementation. A design science research (DSR) approach is adapted for constructing, testing, validating and refining a novel model integrating implementation science and takt production. Within DSR, the theory-based initial model was introduced in a sector-wide workshop and tested, validated and refined through a case study in four Finnish public sector infrastructure organizations. Findings The results indicate that the innovation implementation capabilities of the infrastructure sector organizations have room for improvement and would indeed benefit from the adoption of implementation science and takt production principles. The participants felt that utilizing the constructed model (the TAIMI model) was both beneficial and challenging. Consequently, successful innovation implementation appears to be significantly dependent on organizational and implementation project-specific implementation capabilities. In addition to the implementation model, adapting these new approaches requires improving organizational capabilities and organized coaching. Originality/value Previous research focuses largely on theoretical or instructive settings, whereas this study adds to the understanding of how genuine and effective progress and improved implementation capabilities can be achieved. Together with a conceptual TAIMI model, the gained insights guide on how the tested principles can be utilized in improving infrastructure sector’s productivity.
Purpose This paper aims to propose and validate a framework for conversational AI assistance in BIM-based MEP coordination through a proof-of-concept prototype integrating MCP with Autodesk Navisworks, addressing the post-detection decision support gap. Design/methodology/approach A Design Science Research approach guided development and validation of the framework as the primary artifact, instantiated as an MCP-Navisworks prototype and evaluated through three tests on a real residential building project to identify conditions of use rather than generalizable performance. Findings The framework demonstrated technical viability across all tests. Intelligent grouping produced 20 prioritized element groups from 45 unique clashes, though four clashes were assigned to two groups simultaneously. Across the three resolution cases, the assistant surfaced the coordinator’s adopted solution among its suggested alternatives, though alongside non-adopted options and, for the most complex clash, only after explicit dimensional data was supplied. Research limitations/implications Findings establish technical viability rather than generalizable performance. Performance is constrained by input data quality and current LLM spatial reasoning capabilities. No timing or efficiency measurements were collected. Practical implications Natural-language interaction offers a potential route to supporting post-detection clash analysis. Originality/value Building on an open-source MCP-Navisworks base limited to basic model communication, the study’s specific technical contribution is a Clash Detective integration enabling a conversational AI to read and analyze native clash results, through a three-layer architecture validated as an exploratory proof of concept documenting where LLM-based assistance helps and where it fails.
Purpose The engagement of technology users, providers and investors plays a vital role in shaping innovation pathways within the construction industry. As digital platforms increasingly host professional discussions on emerging technologies, analyzing these narratives is essential for understanding how adoption and investment trends develop. This paper aims to examine how discourse in the construction technology (ConTech) ecosystem reflects funding trends and how firms strategically position themselves. Design/methodology/approach This study adopts an exploratory, abductive mixed-methods approach combining BERTopic-based topic modelling, network analysis and qualitative synthesis. Automated topic detection is complemented by author refinement to identify key themes and priorities in professional digital conversations. Data was collected through purposive sampling from eight construction technology platforms, including institutional websites and LinkedIn blogs authored by verified ConTech professionals. The analysis was conducted in Python using BERTopic and network analysis tools. Findings Knowledge spillovers cluster around 13 coherent topics, with “Venture Capital Strategies and Trends” emerging as the most prominent, underscoring the central role of investment discourse in shaping innovation trajectories. Network analysis confirms this focus, identifying “venture capital” as the most influential connective term across themes. Other key topics include “Robotic Solutions in Construction” and “AEC Digital Innovation and Technology Adoption.” The findings of this study reveal opportunities and persistent barriers to adoption related to integration, compatibility and risk, while emphasizing the importance of collaboration, role clarity and viable business models. Originality/value This study reveals how funding-related discourse coordinates digital transformation in construction by shaping legitimacy, strategic priorities and technology adoption. By mapping discursive centrality and thematic brokerage, it uncovers emerging innovation directions that conventional taxonomies overlook, offering actionable insights for firms, investors and technology startups.
Purpose To effectively support the housing sector, the offsite construction (OSC) supply chain (SC) requires strengthened skills, expanded operational capacity and improved organisational capabilities. However, ongoing SC management (SCM) issues, such as cost overruns, inefficient processes and material waste, continue to limit the housing sector’s adoption of OSC. By addressing these barriers, this study aims to improve OSC-SCM processes while expanding the talent and capability base needed for construction ecosystems. Design/methodology/approach A system dynamics (SD) modelling approach is used to analyse cost and time dynamics under multiple disruption scenarios quantitatively. The model incorporates key barriers, like design inefficiencies, labour shortages, equipment delays and logistical constraints, while also simulating the potential performance gains achievable through skill development, capacity expansion and capability upgrading. Findings The simulation results demonstrate the complex relationships among SC performance, operational capacity and personnel capability in OSC. The findings point to key areas for leverage where focused interventions, such as digital design integration, improved worker training and rapid logistics planning, can increase productivity, cut waste and improve preparedness simultaneously. Originality/value In the OSC context, this research presents a novel integration of SD modelling, showing how digitally driven skill enhancement and capacity building can serve as catalysts for attaining next-generation construction performance and overcoming enduring SCM barriers.
Purpose This paper aims to seek to develop a digital twin (DT)–based framework for identifying systemic delivery risk in construction projects through latent performance–loss states. Conventional monitoring approaches rely primarily on observable indicators such as productivity or schedule deviation, which often reveal problems only after degradation has already propagated through interdependent project activities. The study therefore proposes a monitoring approach that infers underlying system conditions from operational data, enabling earlier detection of emerging risk and supporting more proactive decision-making in complex construction delivery systems. Design/methodology/approach The framework is demonstrated within a representative construction delivery case study using design science research methodology and requires further validation across different project types and live operational environments. The findings establish latent system risk as an inferable condition of interdependent construction delivery systems and provide a basis for further research into adaptive dependency modelling, project health, systemic resilience and intelligent construction control. Findings Across the five scenarios, the framework achieved mean absolute error (MAE) values of 0.06–0.11 and root mean square error values of 0.08–0.15, reducing estimation error by 21%–34% relative to key performance indicator (KPI)-based monitoring. Latent-state inference provided a median detection lead-time gain of 4.2-time units, equivalent to a 35% increase in the available early-warning window. The findings demonstrate that systemic degradation can be inferred before significant deterioration becomes observable through conventional KPIs. Research limitations/implications The study demonstrates the proposed framework using a representative case study, which may limit the generalisability of findings across all project contexts. Further empirical validation across diverse project types and larger dependency networks would strengthen the robustness of the approach. Future research may also explore integration with automated control strategies, advanced sensing technologies and machine learning–based state estimation methods to enhance scalability and predictive capability in DT–enabled construction monitoring systems. Practical implications The framework enables stakeholders to move from reactive monitoring of realised deviations towards earlier identification of emerging system deterioration. By identifying where degradation is developing and how it may propagate through subsystem dependencies, DT-enabled risk intelligence can support project managers, contractors and clients in prioritising earlier and more targeted interventions, with potential to mitigate downstream schedule, productivity and cost consequences. Originality/value The study shifts construction monitoring from observing realised performance deviations towards inferring latent system risk as an evolving condition of interdependent delivery systems. The study proposes an integrated approach to performance loss -state inference, subsystem coupling and DT synchronisation to transform observable project data into systemic risk intelligence. This enables emerging degradation and its propagation to be identified before conventional KPI thresholds are reached, providing an earlier and more targeted basis for construction project control.
