Can Whistleblowing Improve Organizational Effectiveness? Evidence From Financial Reporting Misconduct
Bibliographic record
Abstract
Background While whistleblowing (WB) has attracted growing research interest in recent years, several critical WB-related issues remain underexplored. Purpose This study examines the impact of external WB allegations on a firm’s organizational capital (OC). Such allegations often indicate management’s failure to address employee concerns internally, spotlighting potential deficiencies in internal reporting systems, employee communication, training, and trust in organizational fairness. To mitigate reputational damage, restore employee trust, and prevent future incidents, we posit that WB firms respond by increasing OC investment. Research Design We employ a difference-in-differences approach, comparing OC changes in WB-targeted firms with those in a propensity score-matched control sample. Study Sample Our dataset includes employee WB allegations obtained from OSHA (via a Freedom of Information Act request) and a hand-collected sample from public media. Results We find that WB firms significantly increase OC in the post-allegation period. Additionally, higher OC investment is linked to fewer future WB incidents. The decision to strengthen OC is primarily influenced by employees, long-term institutional investors, and prior OC deficiencies, rather than by WB case credibility or CEO characteristics. Conclusions Our findings indicate the importance of aligning long-term investment strategies and employee benefits with broader corporate goals to foster a responsive and adaptive organizational culture.
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How this classification was reachedexpand
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 | 0.004 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".