Project management challenges in client‐consultant decision‐making: A case for the Ghanaian mining construction industry
Bibliographic record
Abstract
Abstract This study explores the complex decision‐making (DM) dynamics between clients and consultants, a crucial area often neglected in existing research. Utilizing a Multi‐Perspective Approach combined with Multi‐Criteria Decision Analysis, we examine DM challenges such as cost overruns, project delays, and compromised work quality stemming from ineffective DM practices. Key findings highlight the pervasive fear of financial repercussions from poor decisions, which impedes decisive and transparent DM. We offer practical recommendations for managers, propose strategies to strengthen DM processes, enhance project execution frameworks and insights to improve operational efficiency and competitive advantages for Ghanaian mining construction sector stakeholders. This study significantly contributes to the literature on project management by offering targeted solutions and advancing knowledge on DM practices.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".