Salient Complexities of Engaging External Consultants in Information Systems Projects
Bibliographic record
Abstract
Project sponsors have sought to develop the necessary competence to address various challenges they face during project development, implementation, and exploitation by employing different initiatives including the engagement and use of external consultants. However, doing so is associated with a number of consequences, including a significant risk of exacerbating project complexities. With this in mind, we set out in this article to examine the salient differences in the key project complexities between projects engaging consultants and those not engaging consultants. Data are obtained from 146 project management practitioners engaged in projects in Canada and the USA. Data analysis is undertaken using three-way multidimensional scaling. Findings as relates to the key complexities associated with information systems projects, points to the manifestation of six broad dimensions of complexity namely 1) “Variety,” 2) “Control,” 3) “Criticality,” 4) “Scope and repetition,” 5) “Information,” and 6) “Dependence.” As relates to how consultant engagement changes the salience of these key project complexities, we find that consultant engagement leads to more varied and stronger structural complexity and higher salience of interpersonal and organizational complexity.
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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 | 0.019 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".