The Dilemma of Integration versus Autonomy: Knowledge Sharing in Post-Merger IS Development
Bibliographic record
Abstract
Although research acknowledges the role of IS in a merger, it has not addressed the issue of boundary management during the development of ISs aimed at supporting merged organizations. Yet, it has been shown, albeit not in a merger context, that knowledge sharing during IS development involving agents from different communities is critical and difficult. Hence, our study addresses the questions of how agents from merging organizations, engaged in an IS development during post-merger integration (PMI) share knowledge of the work practices required by a specific PMI approach, and of how the resulting IS functionalities are affected by, or do affect the implementation of a PMI approach? Adopting a practice perspective, we aim at developing a theory on knowledge sharing in this context. To do so, we conduct a case study of three IS developments within a merger in the healthcare milieu.
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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 | 0.052 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".