Relevant Factors in The Post-Merger Systems Integration and Information Technology in Brazilian Banks
Why this work is in the frame
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Bibliographic record
Abstract
This article discusses the factors present in post-merger integration of Systems and Information Technology (SIT) that lead to positive and negative results in mergers and acquisitions (M & A). The research comprised three of the largest acquiring banks in Brazil. We adopted two methods of research, qualitative, to operationalize the theoretical concepts and quantitative, to test the hypotheses. We interviewed six executives of banks that held relevant experience in M & A processes. Subsequently, we applied questionnaires to IT professionals who were involved in the SIT integration processes. The results showed that the quality and expertise of the integration teams and managing the integration were the most relevant factors in the processes, with positive results for increased efficiency and the increased capacity of SIT. Negative results were due to failures in exploiting learning opportunities, the loss of employees and the inexpressive record of integration procedures.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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