This article summarizes a civil engineering thesis on the barriers to adopting "smart construction" in a fast-growing Indonesian metropolitan region. If you work in construction tech, proptech, or civic infrastructure, the findings translate well beyond Indonesia.
Surveyed 61 construction industry practitioners in the Gerbangkertosusila metro area (Indonesia)
Used Analytic Hierarchy Process (AHP) to rank barriers and mitigation strategies
Weak government regulation was the single biggest barrier (weight 0.444)
Workforce training was ranked the top mitigation strategy (weight 0.393)
Result consistency ratio: 0.00 — highly reliable data
Why This Matters
Construction is entering what researchers call the Construction 5.0 era: a shift from purely digital tools (BIM, drones, IoT sensors) toward human-centered smart systems that combine AI, robotics, and automation with the people actually doing the building. The theory is well documented. The practice is messier. This study looked at one specific, real-world case: Gerbangkertosusila, a metropolitan cluster of six cities/regencies in East Java, Indonesia (Gresik, Bangkalan, Mojokerto, Surabaya, Sidoarjo, Lamongan) — a region undergoing rapid infrastructure growth, similar to many fast-urbanizing metro areas worldwide.
*Methodology
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The research, by Moch Raffli Trio Romadhoni, Civil Engineering, Universitas 17 Agustus 1945 Surabaya, used AHP (Analytic Hierarchy Process), processed in Expert Choice, to turn qualitative industry opinions into weighted, comparable priorities. It surveyed 61 respondents — contractors, engineers, and project managers across varying company sizes and experience levels — through pairwise comparison surveys that produced priority weights for each barrier and strategy. The consistency ratio came out at 0.00, confirming the responses were logically coherent (AHP typically requires under 0.10 to be considered valid — this came in essentially perfect).
The Three Barrier Categories
Government factors were the most dominant, with a weight of 0.444, led by weak regulation (0.149). Cost factors followed at 0.315, driven mainly by budget constraints (0.116). Workforce factors came in at 0.240, led by a lack of training (0.101). Government factors dominated: weak regulation, insufficient funding support, and slow decision-making processes together made government-related issues nearly as significant as cost and workforce combined. This tracks with a pattern seen in other emerging markets — the technology and the willingness are often there, but the regulatory scaffolding to support adoption at scale lags behind.
What "Smart Construction" Priorities Look Like on the Ground
When respondents ranked implementation indicators by importance, land selection systems came out on top (0.208), reflecting how accurate site and soil data form the foundation for structural stability. Digital construction management followed at 0.205, development planning at 0.204, quality adjustment at 0.197, and implementation monitoring at 0.186. Notably, foundational data — land and soil selection — outranked more "visible" digital tools like monitoring dashboards, a reminder that smart construction isn't just about adding sensors, it's about getting the underlying data pipeline right first.
Recommended Mitigation Strategies
Ranked by priority, workforce training and competency development came first at 0.393, phased technology implementation second at 0.337, and establishing formal smart-construction standards third at 0.271. The clear takeaway is that people, not just tools, are the bottleneck — training ranked above both technology rollout and standardization, a useful signal for anyone building products or policy in this space.
Takeaways for Builders, Policymakers, and Construction Tech Folks
For anyone building construction tech products, adoption friction is less about the technology itself and more about regulatory clarity and workforce readiness — products with built-in training or onboarding may see faster uptake in emerging markets. For policymakers, this is direct evidence that regulatory clarity, not funding alone, is the top lever for accelerating smart construction adoption. For researchers, AHP proves to be a solid, replicable method for turning industry sentiment into prioritized, actionable data, worth considering for similar infrastructure-adoption studies elsewhere.
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