The Impact of Managerial Discretion in Revenue Recognition: A Reexamination*
Bibliographic record
Abstract
ABSTRACT Although the FASB and IASB conceptual frameworks identify relevance and faithful representation as the fundamental qualitative characteristics of useful information, prior research suggests that revenue recognition accounting standards that restrict managerial discretion resulted in improved faithful representation but reduced relevance. We use the adoption of Accounting Standards Update (ASU) 2009‐13 and ASU 2009‐14 to examine the effects of increased managerial discretion to accelerate revenue recognition in multiple‐deliverable arrangements; that is, transactions where vendors sell multiple products or services that are delivered at different points in time. We find that increased discretion results in an increase in the relevance of reported revenues without reducing faithful representation. We further examine whether managers' strategic motivations influence the transparency of ASU 2009‐13 and ASU 2009‐14 adoption disclosure. Although firms providing opaque adoption disclosure do not exhibit a decline in the faithful representation of revenues following the standards' adoption, these firms are more likely than firms providing transparent adoption disclosure to accelerate revenue recognition opportunistically following adoption when incentives are high. These results provide important evidence for assessing whether standards that allow greater discretion in revenue recognition affect the usefulness of revenues and also provide evidence that strategic motivations to preserve flexibility in managing earnings influence the transparency of adoption disclosure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".