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Record W4408846580 · doi:10.1111/1911-3838.12397

The Effect of Operating Leverage on Managers' Capital Investment Decisions<sup>*</sup>

2025· article· en· W4408846580 on OpenAlexvenueno aff
Sang Mok Lee

Bibliographic record

VenueAccounting Perspectives · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsLeverage (statistics)Capital investmentInvestment (military)BusinessOperating leverageCapital (architecture)FinanceComputer sciencePolitical scienceProfitability index

Abstract

fetched live from OpenAlex

ABSTRACT Holding all else constant, a larger proportion of fixed costs in the cost structure (i.e., higher operating leverage) implies a greater profit volatility for a given level of demand fluctuation. In a setting where operating leverage is irrelevant to the choice of investment projects, I experimentally examine whether managers under high operating leverage (as opposed to low operating leverage) choose a less profitable project for lower variability in anticipated earnings. Inconsistent with the hypothesis, I find that the level of operating leverage has an insignificant impact on managers' investment choices, providing preliminary evidence that managers may correctly identify the level of operating leverage as irrelevant. Notably, providing the cost structure information to managers, regardless of whether it pertains to high or low operating leverage, increases the likelihood of selecting the more profitable investment. Additional analyses suggest that the excerpt on operating leverage might prime managers to deliberate more on the investment decisions. Managers who receive the cost structure information dedicate more time to assessing the investment projects, which in turn aids in identifying the more profitable project.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.302
Teacher spread0.285 · 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.

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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