IT project management resources and capabilities: a Delphi study
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the most important IT project management resources and capabilities, and rank them according to the extent to which they are valuable, rare and inimitable. Design/methodology/approach Using a Delphi methodology, the data collection process was conducted with the collaboration of members of academia and professionals with expertise in IT project management. Findings The top ten most important resources/capabilities in IT project management were identified, the majority of which were capabilities; 80 per cent of the identified resources/capabilities were the same in the panel comprised of members of academia and the panel of professionals. Results showed that the two most valuable, rare and inimitable IT project management resources/capabilities were: the capability to understand and manage the needs, expectations, priorities and interests of project stakeholders; and the firm's capability to align IT projects to the strategy and business objectives of the organization. Practical implications This research guides managers in the development of key IT project management intangible resources/capabilities. Originality/value By simultaneously identifying a bundle of important IT project management resources/capabilities, evaluating the extent to which each resource/capability is valuable, rare and inimitable as well as displaying coherence between the results from the different steps of the Delphi method, the resources/capabilities identified in this study are likely to be those few that actually can influence the competitive advantage of the firm. Also, by demonstrating the less important role played by IT resources/capabilities, this study demonstrates that project management is a field of its own.
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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 | 0.046 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".