Enhancing construction efficiency: assessing the values and barriers of integrating e-procurement with BIM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it