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Record W3121545515 · doi:10.19030/jabr.v29i3.7791

The Impact Of Performance-Based CEO And CFO Compensation On Internal Control Quality

2013· article· en· W3121545515 on OpenAlexaff
Kevin Kobelsky, Jee-Hae Lim, Ranjini Jha

Bibliographic record

VenueJournal of Applied Business Research (JABR) · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIncentiveAccountingControl (management)Executive compensationCompensation (psychology)Quality (philosophy)BusinessActuarial scienceEconomicsPsychologyManagementMicroeconomicsSocial psychology

Abstract

fetched live from OpenAlex

In this paper we examine the effects of performance-based compensation on the quality of internal control. We find that the sensitivities of CEO and CFO option portfolios and long-term incentive plans to stock price changes are negatively related to the propensity to report internal control weaknesses between January 2004 and December 2006. We also find that sensitivity is more strongly related to company-level than account-specific control weaknesses. This relation is stronger for CFOs, the officers primarily responsible for the processes generating financial information, and the financial information actually reported by the firm. Despite this positive impact on internal control, compensation sensitivity is positively related to accruals manipulation. Our findings indicate that while SOX disclosures harness the power of compensation schemes to enhance internal control, the controls implemented are not sufficient to eliminate the incentive to manipulate accruals within the bounds of GAAP arising from unvested options and unrestricted equity holdings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.311
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2013
Admission routes1
Has abstractyes

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