The Nexus of Research and Development Intensity with Earnings Management: Empirical Insights from Jordan
Bibliographic record
Abstract
Driven by positive accounting, agency, and political and economic theories, this study examines the relationship between research and development (R&D) intensity and earnings management for listed pharmaceutical companies in the Amman Stock Exchange (ASE) between 2008 and 2021. Employing panel regression methods, the results reveal a positive association between R&D investment and earnings manipulation. Specifically, after two or three R&D delays, the association survived. Moreover, firm size negatively affects earnings management, showing that larger firms have less tendencies to conduct earning manipulation. Furthermore, financial leverage and earnings management are strongly connected, showing that firms may utilize earnings management to avoid credit covenants. The findings emphasize distortions in R&D reporting and profit management within Jordan’s financial reporting practices. Enhancing the accuracy of R&D investment disclosures, minimizing profit manipulation, and fostering greater transparency are crucial. Jordan’s regulators should improve capitalization standards, transparency, auditing, and shareholder activism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".