A framework for benchmarking e-procurement in the AEC industry
Bibliographic record
Abstract
The architecture-engineering-construction (AEC) industry applies a wait-and-see approach because there are no suitable strategic electronic procurement (e-procurement) solutions for the industry. The benefit of electronic procurement is anecdotal for the industry in advance of determining its value. It is necessary to evaluate e-procurement and measure the added value during implementation in order to motivate users to adopt it. In this research project a survey was conducted to provide a clearer picture of the level of penetration of e-procurement. Functional benchmarking parameters were established to quantify the values of e-procurement and an adoption of these parameters in the AEC industry was presented for a later comparison between the construction sector and manufacturing industry. The results of the comparison indicate potential opportunities for e-procurement in the AEC industry. However, there is a conflict with the real situation in the industry in that e-procurement service providers are focused on the supply side not on the demand side; and there is another conflict with current trends in that e-procurement technological providers are focus on individual enterprises not on industry associations. To overcome the conflicts, an electronic business platform is suggested as a strategic e-procurement model for the 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.
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 | 0.051 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".