Ambiguity acceptance and translation skills in the project management literature
Bibliographic record
Abstract
Purpose The project management literature provides a fairly united picture of the importance of projects being successful. One success factor is represented by project managers themselves, whose personality, skills, knowledge, competencies, and traits affect project success. To better understand various project manager types, the purpose of this paper is to review the extant project management literature and propose a framework for categorising project managers based on the traits that they possess or lack. Design/methodology/approach The research commenced with identifying and collecting articles from the academic project management literature. The articles were then coded to identify different competencies and traits that a project manager needs to be successful. Based on this analysis, a framework with four main project manager types was developed. Findings The results indicate that ambiguity acceptance and translation skills are two important dimensions that project managers need to be successful. The four project manager types were arranged around two dimensions. Research limitations/implications The framework presented is based on previous research. Empirical testing of the proposed framework would be a promising direction for future research. Practical implications The framework assists reflective practitioners in identifying what kind of project manager they currently are, suggesting how they might transition into a different project manager type to increase their project management success rate. Originality/value This paper conceptualises project managers and how their personal traits relate to project success. It offers practical help to project managers in understanding their strengths and limitations, and how to become a different type of project manager.
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 | 0.033 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".