Archetypes and the Logic of Management - How assumptions on ERP systems influence management actions
Bibliographic record
Abstract
The journey with enterprise resource planning systems has taken us beyond implementation, into the second wave of ERP. Now, after running in some years, it is interesting how the ERP system is managed and what role it plays in the organization. These questions are addressed in this paper. Through seven case-studies we found similarities and differences in the patterns of ERP management. The key characteristics, with respect to which ERP management differs, are associated with an archetypal interpretive scheme that we conceive as embodied organizational motivation and architecture. The empirical analysis coalesced in our conception of three alternative archetypes - which we call the supporter, the driver and the co-player. These archetypes are illustrated with material from our case-studies. The archetypes are believed to play an important role in conveying the essential differences of the ways in which organizations manage their ERP system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".