Complexity Theory: Insights from a Canadian ERP Project Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research paper explores complexity theory based on insights from an Enterprise Resource Planning (ERP) implementation in the Canadian oil and gas industry. The qualitative exploratory case study was conducted in a Canadian case organization using a semi-structured interview guide with a total of twenty interviews from members of four project role groups of senior leaders, project managers, project team members, and business users. Besides interview responses, the study also collected and reviewed ERP project documents for triangulation purposes. The research showed the importance of complexity theory to ERP projects, and the relationship between critical challenges and complex categories of human behavior, system behavior, and ambiguity. The study findings also evoked rich and comprehensive data related to the phenomenon of critical challenges in ERP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it