Enhancing construction efficiency: assessing the values and barriers of integrating e-procurement with BIM
Bibliographic record
Abstract
This study aims to analyze the influence of integrating e-procurement and Building Information Modeling (BIM) on the construction industry, focusing on its potential to enhance efficiency, collaboration, and project delivery while addressing key challenges. A quantitative approach was adopted, using a structured questionnaire survey to collect data from respondents affiliated with client, contractor, and consultant organizations. The analysis employed advanced techniques such as Interaction Coefficient (IC), Synergistic Contribution (SC), Dynamic Influence and Dependency analysis, Forecast Impact and Resilience analysis, System Dynamics Simulations and Scenario Analysis. The findings highlight key benefits, including enhanced quality, cost control, and increased efficiency, which streamline project execution and optimize resource management. However, barriers such as lack of interoperability, incomplete standards, and organizational resistance hinder widespread adoption. Technological, managerial, and regulatory factors drive these challenges. The study suggests that addressing these barriers through interoperability frameworks, standardized protocols, and stakeholder engagement can unlock BIM’s full potential. The results offer valuable insights for policymakers and industry stakeholders, emphasizing the importance of overcoming these challenges to realize the full benefits of BIM-integrated e-procurement. When combined with short-term improvements and long-term innovations, BIM-integrated e-procurement can be a transformative tool in the construction industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".