Capturing Users' Tacit Knowledge in 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
This study examines capturing users' tacit knowledge in enterprise resource planning (ERP) systems. To mitigate the risks in implementing ERP systems, a knowledge based approach is followed. The ERP implementation team depends upon users for their knowledge to understand the business rules and processes required for the ERP systems. The value of ERP implementation is increased when users' tacit knowledge has been integrated into ERP systems. This paper attempts to understand how Canadian organisations are capturing the users' tacit knowledge in ERP implementation. A case study methodology is followed to accomplish the research objective. Three organisations from telecommunication, government, and retail sectors participated in the study. For data collection, semi-structured interviews were conducted with four to six respondents from each firm. The findings about tacit knowledge sharing in three firms that have implemented ERP systems are presented. The findings are categorised as follows: ERP adoption by all three firms, implemented ERP modules, users' tacit knowledge capturing and conversion, activities and approaches, users' tacit knowledge for interim modification and post-implementation. The lessons learned are given by presenting a cross-comparison of three 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| 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