Identifying the interests of stakeholders in large construction projects: Based on Justification theory of Boltanski and Thévenot
Bibliographic record
Abstract
In large construction projects, many stakeholders are involved, often with different interests. They look at the interests through the lenses of their conflicting worldviews, leading to the formation of different worlds that often have tensions among them. For example, to be viable, the projects must find a compromise between competition and collaboration. Hence, success in developing and implementing large construction projects requires analyzing the justifications of the world of stakeholders, identifying conflicts and creating agreements among them. Thus, the main purpose of this paper is to use Boltanski and Thévenot's theory to review the justifications of stakeholders’ various and understand the tensions and compromises in large construction projects. The research approach is exploratory qualitative, realized using a multiple case study. In this process, five cases were selected as five large construction projects from the four countries of Iran, Turkey, India and Ethiopia and were analyzed based on the data extracted from written, visual, and audio sources. The results indicate that while six stakeholder groups are identified in the sources, content analysis and clear evidence reveal that the industry, construction, and market groups dominate in large construction projects. In other words, the stakeholders often justify their benefits and losses through the lens of these three worlds. In addition, the research discusses tensions and possible compromises in large construction projects. The results of this research improve the insight and knowledge of project managers and contribute to the success of large construction projects. However, it faces limitations in terms of methodology and data adequacy.
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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".