Megaproject Management Research: The Status Quo and Future Directions
Bibliographic record
Abstract
Megaproject practices worldwide have triggered increasing research in megaproject management issues and led to an increasing number of papers being published during the last decade. However, it is demonstrated by the literature that there is no systematic examination on research development in the discipline of megaproject management, and consequently it is very difficult for scholars to quickly understand and grasp the research trend. Therefore, a research question naturally comes out, i.e., what is the status quo of megaproject management research and what are the research directions worthy of further investigation? This study aims to answer the question by conducting a systematic examination of the research development in the discipline of megaproject management. A total of 117 relevant articles, identified from six major international journals between 2009 and 2021, were analyzed based on the number of papers published annually, main author contributions, citations, categorization of the research methods and data analysis methods adopted, and research topics covered. The results indicated that developed countries, such as Australia, Canada, the United States, and the United Kingdom, have enjoyed significant advantages in terms of megaproject management research. It also revealed that more sophisticated views and theory have been used effectively, rather than only basic qualitative methods, in a number of studies on megaproject management. Future studies on megaproject management will be led globally, where megaprojects will remain designed and built to better built environments. In addition, continuous in-depth research on related topics can promote innovation in megaproject management to achieve sustainable megaproject development. Megaproject management will continue to be a hot research topic in the future; in particular, megaproject investment and finance management have emerged as new challenging topics. The findings can be valuable for both industry practitioners and researchers to gain deeper understanding of the current status and future directions of megaproject management research.
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 | 0.085 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".