Tacit Knowledge Sharing in Geographically Distributed Enterprise Resources Planning (ERP) Implementation: An Exploratory Multi-Site Case Study
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
Organisations that implement Enterprise Resources Planning (ERP) software packages are making a big commitment in terms of both time and money. Realising the ERP benefits, some organisations have successfully implemented while others have struggled, settled for minimum returns, and abandoned the system. Especially in a Geographically Distributed Environment (GDE), ERP implementation is more risky. To mitigate the risks, a knowledge sharing framework is suggested to be put in place during ERP implementation phases. The ERP implementation requires more knowledge about business processes, transaction rules, organisational structure, and other related transactions. Based on findings in an extensive study of three Canadian organisations that have gone through ERP implementation phases, this study examines tacit knowledge sharing in design, configuration, and testing of ERP systems. The lessons learned and knowledge sharing activities are given by presenting a cross-comparison of the case studies.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| 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