Strategic Integrity Management as a Dynamic Capability
Bibliographic record
Abstract
Increasing societal expectations regarding responsible business conduct as well as an increasingly fierce enforcement environment are placing integrity and compliance highly on the list of management priorities. However, although many companies are investing serious efforts into compliance programs and are increasing the headcount within their compliance organizations it seems to be striking that severe ethical breakdowns still happen on a very regular basis. The reason may well be that strongly relying on classical elements of compliance programs to prevent misconduct such as establishing numerous policies, strengthening awareness training and increasing monitoring mechanisms works like trying to win the Tour de France with flat tires. Typically, structural causes are the driving forces behind systemic misconduct. These include; unbalanced incentives systems that determine bonuses, salary increases or career progression without any consideration of moral behaviors; a lack of responsible leadership; or a climate in which speaking-up is not an accepted practice but instead, represents a career risk that could lead to repercussions. Therefore, we suggest that in the interest of further preventing and reducing cases of misconduct a more analytic and less ideology driven discussion about the real and systemic root causes of integrity issues needs to take place. Unethical conduct should not just be viewed as lack of integrity or lack of certain character qualities of some individual employees. Such a perspective does not recognize the complexity of most corporate integrity or compliance issues. Research has shown that the apparent frequency of ethical misconduct is being caused by organizational factors that create enormous psychological pressures on individuals. This then leads to situations where even managers who are considered highly moral deviate from everyday ethical norms. People that constantly claim that compliance failures are mainly caused by the ethical misconduct of individuals – let’s call tem bad apples – and simplistically refer to the lack of personal integrity seem to confuse the real root causes. They may generate some kind of moral alarm but are not helpful in terms of finding systematic and sustainable solutions to the problem. In this chapter we describe how dynamic integrity management processes can be integrated into the game-changing organizational systems in order to avoid negative conduct. Further, we will build a road map of how integrity management can actively drive positive effects on firm performance and we describe how integrity management can be used dynamically as a strategic tool that creates positive effects on firm performance.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".