Project success and project team management: Evidence from capital projects in the process industries
Bibliographic record
Abstract
Abstract Efficient project execution is a key business objective in many domains and particularly so for capital projects in the process industries, but existing project management research gives little direction about how project team factors influence three important capital project outcomes: cost, schedule, and operability. After an extensive cross‐disciplinary review of the general team and project management literatures, we constructed and tested a theoretically based, five‐dimensional model of organizational context, project team design, project team leadership, project team processes, and project outcome factors. We examined the model by means of an empirical study of 56 newly completed capital projects executed by 15 Fortune 500 companies in the process industries. The results indicate the value of disaggregating project outcomes for research purposes. Different bundles of project team factors were found to drive project cost, schedule, and operability. Project team efficacy, cross‐functional project teams, autonomous project team structure, and virtual office usage were the strongest predictors of project cost effectiveness. Continuity of project leadership, cross‐functional project teams, and project manager incentives were the strongest predictors of project construction schedule. In contrast, clear project goals and an office design to facilitate effective communication were the main predictors of plant operability. Implications of these findings for researchers and project practitioners are discussed. One major practical implication of our findings is that project managers need to clearly focus and prioritize their goals for each project so they can adopt the appropriate bundles of project team practices that will facilitate their goal achievement.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".