The Impact of Organizational Capital on Cost Stickiness: Evidence from Japanese Firms
Bibliographic record
Abstract
This study examined the impact of organizational capital (OC) on the cost stickiness of Japanese firms and analyzed whether this effect varies with the magnitude of sales changes. Using 12,727 firm-year observations from Tokyo Stock Exchange-listed firms between 2007 and 2024, we estimated the economic value of OC by capitalizing and amortizing selling, general, and administrative (SG&A) expenses, then classified firms into high- and low-OC groups based on the median. Cost stickiness was then compared across groups using the basic, ABJ, and extended models, with robustness checks based on adjusted OC and two-way fixed effects models. The results indicate that high-OC firms exhibit stronger cost stickiness, while low-OC firms display weaker or insignificant stickiness. The effect depends on the magnitude of sales fluctuations: stickiness is pronounced under small changes but diminishes or disappears under larger shocks. Overall, this study contributes by highlighting the role of organizational resources in shaping asymmetric cost behavior, extending explanations beyond adjustment costs or managerial incentives, and providing novel evidence from Japan, where firms generally exhibit cost stickiness regardless of OC level, reflecting institutional and cultural contexts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".