Project control: an exploratory study of levers of control in the context of managing projects
Bibliographic record
Abstract
Purpose This paper aims to examine the levers of control (LOC) framework in the context of managing projects. The authors explore the impact of diagnostic systems, interactive systems, beliefs systems and boundary systems on project performance and explore the association between control levers. Design/methodology/approach Using data gathered from 113 project managers representing 38 organizations, the authors used the partial least squares path modelling algorithm to analyse the data. Findings The analysis validates the LOC framework in the context of managing projects and reveals the interrelatedness of control systems and their impact on project performance. Research limitations/implications The conclusions support the current emphasis on performance reporting and change control and highlight the need to consider the interdependencies between control levers. Originality/value This study re-conceptualizes project control by using the LOC framework in the context of managing projects as temporary organizations. This provides a model for investigating and understanding project management control systems that consider the interaction of control mechanisms. Furthermore, the associations between the four control systems and project performance are examined, rather than individual mechanisms in isolation.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".