The Impact of Cost Stickiness on R&D Investment and Corporate Performance: An Empirical Analysis of Japanese Firms
Bibliographic record
Abstract
This study examines the impact of cost stickiness on research and development (R&D) investment and corporate performance in Japanese firms. Additionally, it investigates the moderating effect of managerial overconfidence and financial slack. To do so, we analysed a sample of 4877 observations from Japanese firms listed on the Tokyo Stock Exchange between 2014 and 2020. The results show that cost stickiness generally promotes R&D investment while negatively affecting corporate performance. Further, although managerial overconfidence does not moderate the relationship between cost stickiness and R&D investment, it weakens the negative effect of cost stickiness on corporate performance. Meanwhile, financial slack strengthens the positive impact of cost stickiness on R&D investment, but it does not moderate the relationship between cost stickiness and corporate performance. These findings provide strategic insights into resource allocation behaviour in driving innovation and influencing corporate outcomes in the Japanese market context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".