Effects of the environment on illegal cartel activity
Bibliographic record
Abstract
Purpose – Using resource dependency theory, the purpose of this paper is to examine what elements in the business environment may be associated with the formation and continuance of cartels. Design/methodology/approach – The authors employ a unique data set of 148 cartel data points from the 1970s to 2008 which have at least one American company involved to quantitatively test causal relationships. The authors also interview key class action anti-trust attorneys for their views and opinions on the impact of these environmental factors on cartel formation and continuance. Findings – The authors find statistically significant relationships between the pursuit and maintenance of industry profits and the dynamism in the industry, and illegal behavior as represented through price fixing by business cartels. The authors find that in the attorneys’ opinion, it is also the pursuit of individual corporate profits and munificence that are associated with these cartels. Practical implications – This research furthers the understanding of organizational deviance which is critical given its impact on organizations, individuals, regulators, law enforcement, and the general public. Originality/value – This research is a first step in considering cartel activity in a way that encompasses external influences in a new and innovative manner and as a tool to help researchers and practitioners better understand how organizational deviance, as manifested through illegal corporate activity, can be anticipated, identified, and prevented.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".