TACIT KNOWLEDGE SHARING IN DESIGNING, CONSTRUCTING, AND TESTING OF ERP SYSTEMS: AN EXPLORATORY MULTI-SITE CASE
Bibliographic record
Abstract
This study examines tacit knowledge sharing in designing, constructing, and testing of ERP Systems in a geographically distributed environment. To mitigate the risks in implementing ERP systems, a knowledge based approach is followed. The ERP implementation team depends upon knowledge to understand the business rules and processes required for the ERP systems. The value of ERP implementation is increased when tacit knowledge has been integrated into ERP systems. This paper attempts to understand how Canadian organizations are sharing the tacit knowledge in geographically distributed ERP implementation environment. A case study methodology is followed to accomplish the research objective. Three organizations 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 categorized into three phases: tacit knowledge sharing in design, construction and testing of ERP systems. The lessons learned are given by presenting a cross-comparison of the three case studies. Based on the case analysis, the activities for tacit knowledge sharing in geographically distributed ERP implementation environment are given. Key words: Enterprise Resources Planning, Geographically Distributed environment, tacit knowledge sharing, ERP design,
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".