Information Systems Project Manager Soft Competencies: A Project-Phase Investigation
Bibliographic record
Abstract
This article investigates the soft competencies by project phase that information systems (IS) project managers require for project success. The authors conducted 33 qualitative interviews to collect data from a sample of 22 IS project managers and business leaders located in Calgary, Alberta, Canada. The authors identified the key competencies for each of the IS project phases (initiation, planning, implementation, and close-out). The competencies were sorted into competency categories: personal attributes (e.g., eye for details), communication (e.g., effective questioning), leadership (e.g., create an effective project environment), negotiations (e.g., consensus building), professionalism (e.g., lifelong learning), social skills (e.g., charisma), and project management competencies (e.g., manage expectations). Each of the most important competencies is discussed and interconnections among competencies identified. How this research can be used by the practitioner and academic communities and the broader implications of this research are examined.
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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".