Corporate Wrongdoing and Board Leadership Structure: The Stories of WestJet and Hewlett-Packard
Bibliographic record
Abstract
Based on underpinnings from the extant literature on negotiated order theory and board leadership structure, we propose a model to explain the relationship between board leadership structure, corporate wrongdoing (Internet snooping and pretesting/social engineering), and company performance. In order to illustrate our proposed model, we develop two mini cases on WestJet in Canada and Hewlett Packard in the United States, where serious corporate wrongdoings had taken place at the behest of the board leaders. The comparison of the cases suggests that both CEO duality and CEO/Chair split can contribute equally to corporate wrongdoing. Consistent with the theoretical model, the comparison also illustrates that corporations are able to withstand the adverse effects of wrongdoings and regain investor confidence and profitability through the symbolic and substantive reversal of their leadership structures following a scandal. The study’s implications are given.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.030 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.015 |
| 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".