Accounting for intangible assets – insights from meta-analysis of R&D research
Bibliographic record
Abstract
Purpose The knowledge- and Internet-based economy demands a reexamination of the accounting treatment for intangibles and a thorough understanding of the empirical evidence on this topic. Design/methodology/approach The study reviews the literature on research and development (R&D), a specific internally developed intangible asset, using meta-analysis techniques that allow to highlight the areas of consensus and disagreement in quantitative empirical results. The literature the authors review addresses four main research questions on (1) the determinants of the decision to capitalize R&D, (2) stock market-based outcomes of capitalizing R&D, (3) firm-based outcomes related to expensing R&D and (4) stock market-based outcomes of expensing R&D. Findings The authors find higher value relevance of capitalized compared with expensed R&D. There is, however, little robust evidence on the determinants of the capitalization decision and the characteristics of capitalizers. Originality/value The authors conclude by highlighting future research that can allow accounting academics to contribute to standard setting.
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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 | 0.084 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".