Unveiling global research trends in construction productivity: a scientometric analysis of twenty-first century research
Bibliographic record
Abstract
Abstract Construction productivity research has exploded in the twenty-first century, captivating scholars worldwide. To navigate this burgeoning field, this study utilizes a scientometric analysis approach to identify and evaluate 710 academic articles, examining geographical publication patterns, author contributions, leading journals, keyword co-occurrences, and key findings from previous studies. The results reveal that the United States, Canada, and Australia are the top contributors in terms of publication output. The Journal of Construction Engineering and Management, Automation in Construction, and Construction Management & Economics emerged as leading journals. Keyword analysis finds “productivity,” “construction industry,” and “project management” to be the most prevalent. Notably, research relies on empirical methods like questionnaires and utilizes popular measures such as relative importance index, factor analysis, and regression analysis. Additionally, smart construction and sustainable cities appear as promising paradigms for achieving sustainable productivity. Furthermore, prior studies advocate for workforce upskilling, enhanced motivation, work environment improvements, strengthened site management, and embraced technological advancements to boost construction productivity. This paper enriches the existing body of knowledge by mapping the global research landscape on construction productivity, uncovering emerging trends, identifying influential contributors, and highlighting promising areas for future research. In practical terms, it provides construction practitioners with valuable insights into emerging technologies and promising management approaches that can enhance productivity and optimize construction processes.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.152 | 0.201 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".