More Information, More Risk? The Hidden Downside of Smart Construction

Smart construction is accelerating. IoT sensors monitor job sites in real time, BIM models every component in 3D, and big data coordinates entire supply chains. But as ICT-based information sharing becomes more sophisticated, does it actually reduce risk — or does it create new pathways for risk to spread?

This study analyzes the risk structure of an ICT-based information sharing network in a prefabricated building supply chain using Social Network Analysis (SNA). (Zhu & Li, 2025)

Background

Technology adoption outpaced risk governance

Prefabricated building (PB) supply chains have embraced Industry 4.0 technologies rapidly. IoT, BIM, big data, and cloud computing now connect design, manufacturing, logistics, and construction in a continuous flow of shared data. In principle, this means every stakeholder makes decisions based on the same information — faster, more accurately, and with less waste.

But existing research has mostly treated risk as a checklist: identify the hazards at each supply chain stage, list them, and assign responsibility. What it hasn’t addressed is what happens when tightly connected stakeholders share information through a dense network. A disruption at one node can propagate quickly through the entire system. The more interconnected the network, the faster risk travels.

There has been little quantitative work on which stakeholders sit at the center of that risk propagation, or which specific risk factors pose the greatest systemic threat. That’s the gap this study addresses.

Key Idea

Turn the information-sharing network into a risk map

The study’s central insight is that the ICT-based information sharing structure (IS-PBSC) is not just a communication channel — it’s also a risk propagation channel. Information flows and risk flows follow the same paths.

The researchers modeled the supply chain’s information network as a directed graph, with 8 stakeholder groups as structural anchors: Government (S1), Developer (S2), Designer (S3), Manufacturer (S4), Contractor (S5), Logistics Provider (S6), Supervisor (S7), and End User (S8). Each stakeholder carries its own risk factors, and the directional links between them represent how those risks flow from one party to another.

To measure each node’s influence, the study uses Status Centrality — a metric that captures not just direct connections, but the cumulative indirect influence a node exerts across the full network. A node with high out-status centrality has the greatest potential to propagate risk throughout the system.

Solution

To quantify the risk structure of the ICT-based prefabricated building supply chain, the researchers applied Social Network Analysis (SNA) using NetMiner 4.

Step 1. Identifying risk factors

Through a combination of literature review and expert interviews, the team identified 49 risk nodes spanning the entire supply chain. These were distributed across the 8 stakeholder groups based on each party’s role in the information sharing network.

Step 2. Constructing the IS-PBSC risk network

Using NetMiner 4, the researchers built a directed network with 49 nodes and 451 directional links. The initial network density was 0.192 — meaning roughly 19% of all theoretically possible connections between risk nodes were present. Network cohesion measured 0.564.

Step 3. Analyzing critical risk nodes

Status centrality scores were calculated for every node, capturing both direct and indirect influence within the network. The analysis identified S1R27 — “lack of information management specification,” a risk factor belonging to the Government stakeholder group (S1) — as the most influential node in the network, with an out-status centrality of 3.090007.

Step 4. Comparing stakeholder groups

Centrality metrics were compared across all 8 stakeholder groups. Government (S1) and Developer (S2) consistently occupied the most central positions — meaning their risk factors have the greatest downstream impact on the rest of the supply chain.

Step 5. Simulating mitigation strategies

The team simulated the effect of removing or mitigating the most critical risk nodes. After applying targeted mitigation strategies, network density dropped from 0.192 to 0.113 — a 41.15% reduction. Cohesion fell from 0.564 to 0.424, a 24.6% decrease. These numbers confirm that addressing the right nodes can substantially weaken the entire risk propagation structure.

Conclusion

Governance first, technology second

The findings reveal a paradox at the heart of smart construction: the more tightly integrated an ICT-based supply chain becomes, the more efficiently risk can spread through it. The highest-centrality risk factor in the entire network — S1R27, the absence of information management standards — belongs to the government stakeholder group. This is a clear signal: before scaling up information-sharing technology, the regulatory and governance infrastructure must be in place.

The fact that Government (S1) and Developer (S2) dominate the risk network also has practical implications. Improvements to information governance policies and contract structures within these two groups can produce cascading reductions in risk across the entire supply chain. The simulation results bear this out — targeted mitigation at the critical nodes reduced network density by more than 40%.

This study matters because it reframes supply chain risk as a systems-level problem, not a series of isolated issues at individual stages. For construction project managers and policymakers, SNA provides a data-driven framework for deciding where to intervene first — and how to measure the effect.

**☞ Try for Free ☞ NetMiner Help Center**

NetMiner | CYRAM Network Science · Social Network Analysis · Graph Analytics https://www.netminer.com

References

• Zhu, X., & Li, Y. (2025). Intelligent construction technology based information sharing network for prefabricated building supply chain. Kybernetes, 54(9), 5158–5180. https://doi.org/10.1108/K-02-2024-0409