Where lean construction and offsite construction meet: a bibliographic scientometric analysis
Bibliographic record
Abstract
Purpose Improving construction projects' performance through innovative approaches such as lean construction (LC) and offsite construction (OSC) methods are at the centre of various debates. However, there is a limited understanding of the current link between LC and OSC approaches. This study aims to conduct a scientometric analysis on LC and OSC research to unpack and establish the nexus and suggest future research focus. Design/methodology/approach Scientometric analysis was used to systematically examine existing literature on LC and OSC to identify possible connections. Relevant publications were extracted from the Scopus database, using inclusion and exclusion criteria. VOSviewer software was used as a visualisation technique to analyse and map the interrelations and connections of the concepts being studied. Bibliograhic data on the 68 selected papers were extracted from the Scopus database. Findings The search results cover the period between 2003 and 2021. Descriptive statistics show that the number of published papers has increased yearly. Researchers in the USA and Canada are the most productive authors regarding the number of published papers. The directions for future research suggested are the need to identify best practices for integrating LC and OSC methods, the need for more interdisciplinary and cross-country collaboration among researchers, the use of alternative research methods will provide a better understanding of the benefit of integrating LC and OSC techniques and more research is needed to showcase how the use of lean and offsite construction can facilitate the attainment of net-zero in the construction industry. Originality/value This study provides insights into the trends and gaps in knowledge on integrating LC and OSC methods and offers valuable insights to scholars and practitioners in integrating LC and OSC principles. This knowledge is vital for identifying strategies to improve the outcome of construction projects and contribute to the sustainable socio-economic development of cities across the globe.
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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 | Observational | 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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.216 | 0.288 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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.
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".