Siemens’ Evolving Approach to Internalizing Organization-wide Compliance Learning in the Wake of its Corruption Scandal – Theoretical & Practical Insights Based on a Study of Siemens Canada (2007-2017)
Bibliographic record
Abstract
This exploratory case study applies concepts from organizational learning literature, specifically organizational routines, to study the implementation, operation, governance and evolution of compliance practices at Siemens Canada between 2007 and 2017. These routines – which took the form of new institutional arrangements, rule instruments and processes – were implemented across Siemens' divisions and its numerous subsidiaries around the world after the company’s corruption scandal was exposed in late 2006. Siemens' compliance-oriented response to its scandal signified a major organizational learning effort to align the company with a growing international consensus against the use of bribery and other types of corrupt practices by public and private organizations. "Inside-out" studies that examine the complexities and nuances of organizational governance are rare, particularly in the case of a subsidiary of a large multinational corporation (MNC). Hence, based on a series of interviews with a diverse group of employees at Siemens Canada, this study aims to contribute both practical and theoretical insights to organizational learning literature.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".