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Record W4233858530 · doi:10.17722/ijme.v10i1.949

A Review of Employee Stock Option Plans: Panacea or Pandora’s Box for Firm Performance

2017· review· en· W4233858530 on OpenAlexvenueno aff
Neha Kalra, Rajesh Bagga

Bibliographic record

VenueInternational Journal of Management Excellence · 2017
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsStock optionsStock (firearms)GlobeShareholderNon-qualified stock optionCompensation of employeesBusinessVestingExecutive compensationRestricted stockEconomicsAccountingCorporate governanceFinanceCompensation (psychology)Political scienceEngineering

Abstract

fetched live from OpenAlex

Employee stock option Plans (ESOPs) have gathered enormous attention in recent decades and have become the most controversial component of the compensation package. Organizations around the globe have been using ESOPs to compensate their employees at managerial and non-managerial levels. While traditionally the stock options were reserved for top management employees, lately there has been strong growth of broad-based plans primarily to increase firm value. Recent literature examining the effects of broad-based stock options are not limited to executive but available for all employees (Core and Guay, 2001; Oyer and Schaefer, 2005; Hallock and Olson, 2010, etc.). However, the shareholders have become increasingly apprehensive about the size and proliferation of adoption of stock option plans. Accordingly, they have been an issue of debate in both academic research and practice circles. The present paper outlines the theoretical foundations behind the use of ESOPs in the compensation mix and strives to address the controversy of whether or not stock options adoptions result in enhancement in firm value. Though the evidence is mixed on the implications of ESOPs, however, there exists robust support for a positive interrelationship between the adoption of these plans and firm performance for large sized firms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.124
GPT teacher head0.351
Teacher spread0.228 · 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 designOther design
Domainnot available
GenreReview

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

Citations2
Published2017
Admission routes1
Has abstractyes

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