The Core Competencies Necessary for Global Information Technology Project Management
Bibliographic record
Abstract
Global information technology (IT) project management organizations can create more value in their operations by presenting the core competencies possessed by global IT project managers (PMs) who are successful in their respective projects. The purpose of this multiple case study was to explore the core competencies and business strategies that IT PMs use to meet global IT project deadlines and budgets. This study involved 5 IT PMs from the Pacific Northwest (United States and Canada) who increased the quality of outsourced IT projects from 4 different companies. The data collection method included in-person semistructured interviews of participants and review of existing company data. Thematic analysis of data included the use of member checking to ensure that the results of this study accurately reflected the experiences of the participants. The conceptual framework that guided the research was organizational learning theory. Two major themes were uncovered during data analysis; the first was global IT project management barriers and reasons for failure, and the second was competencies and strategies for successful global IT project management. Additionally, 4 subthemes were identified: lack of communication and quality, issues with culture and time, mindful of cost and coordination, and cultural awareness and communication. This study shows how successful IT projects benefit organizations and society with better products and services at lower costs. The findings may assist IT PMs in applying core competencies and business strategies to manage global IT projects to meet project deadlines and proposed budgets, which may, in turn, help companies contribute to corporate social responsibility efforts through improvements in ethical standards and international norms.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".