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Record W4414750689 · doi:10.3390/jrfm18100559

The Impact of Organizational Capital on Cost Stickiness: Evidence from Japanese Firms

2025· article· en· W4414750689 on OpenAlexvenueno aff
Shoichiro Hosomi, Gongye Ge

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsStock (firearms)Robustness (evolution)Cost of capitalOrganizational capitalCapital expenditureValue (mathematics)Enterprise value

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.230
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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