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Record W3143422143 · doi:10.2308/jmar-18-055

CFO Overconfidence and Cost Behavior

2021· article· en· W3143422143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management Accounting Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsOverconfidence effectIncentiveAgency costResource (disambiguation)Agency (philosophy)MicroeconomicsSample (material)Principal–agent problemResource dependence theoryBusinessEconomicsActuarial sciencePsychologyFinanceSocial psychologyCorporate governanceComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Using a large sample of U.S. firms, we provide evidence of the effect of CFO overconfidence on firms' resource adjustment decisions. After controlling for CEO overconfidence, we find CFO overconfidence is positively associated with cost stickiness. We also find that CFO power relative to the CEO increases the positive association between CFO overconfidence and cost stickiness. Our study contributes to our understanding of the important role of CFOs in operational decisions such as resource adjustment decisions. We also extend the literature on cost behavior by highlighting managerial characteristics as an important determinant of resource adjustment decisions. Our study has important practical implications. Unlike resource adjustment decisions driven by agency problems or other incentive-related issues, such decisions driven by managerial overconfidence cannot be addressed with incentive contract designs. Promising ways to mitigate overconfidence-driven resource adjustment decisions include making overconfident managers aware of their potential behavioral biases and challenging their expectations.

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.

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)0.000
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 score0.282
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.067
GPT teacher head0.328
Teacher spread0.261 · 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