Penny Wise and Pound Foolish: Does Striving to Meet Earnings Expectations by Manipulating Real Activities Trigger Product Recalls?
Bibliographic record
Abstract
We examine whether managers’ activities in striving to reach earnings targets through real earnings manipulation affect their firms’ product recalls. Our evidence implies that firms suspected of manipulating real activities in trying to meet earnings benchmarks exhibit a higher likelihood and frequency of product recalls. In cross-sectional results consistent with expectations, we find that the impact of exploiting real activities to attain earnings benchmarks on product recalls intensifies for firms whose managers have stronger incentives to manage earnings and subsides for firms subject to greater customer power and firms with more growth opportunities. Additional analysis shows that lowering product quality to meet or beat earnings expectations undermines firms’ future performance. This paper was accepted by Ranjani Krishnan, accounting. Funding: Y. Chen acknowledges the General Research Fund of the Research Grants Council of Hong Kong [Project 15504219] for financial support. L. Ma acknowledges financial support from the Ministry of Education Project of Humanities and Social Sciences [Project 22YJC630099] and the Young Scholars Grant Program of the University of International Business and Economics [Project 20YQ06]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04035 .
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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 | 0.003 | 0.028 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".