The ERP post-implementation stage: a knowledge transfer challenge
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 paper examines the knowledge transfer process in ERP post-implementation projects, and specifically between the ERP project teams and the IT support team. Case studies were conducted in three large organizations and data was collected via semi-structured interviews. Descriptive and graphical representations were used to analyze knowledge transfer processes for each case and a cross-case analysis was performed. Results from this exploratory study shed light on the relation between the ERP evolution structure and the use of knowledge transfer mechanisms based on different types of knowledge (functional and technical). This paper highlights the necessity of relying on both formal and informal knowledge transfer mechanisms to cover recurring and ad hoc exchanges between the different stakeholders responsible for the evolution of an ERP. The paper also highlights the impact of the ERP integrator and its different inclusion strategies that are critical for the knowledge being shared by the ERP project stakeholders.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it