MétaCan
Menu
Back to cohort

Adoption and Coverage of Performance‐Related Pay during Institutional Change: An Integration of Institutional and Agency Theories

2010· article· en· W2120369752 on OpenAlexaff
Sung‐Choon Kang, Yoshio Yanadori

Bibliographic record

VenueJournal of Management Studies · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRemunerationLegitimacyAgency (philosophy)PoliticsInstitutional theorySample (material)Principal–agent problemPublic economicsInstitutionalismBusinessEmpirical researchEarly adopterEconomicsPublic relationsMarketingCorporate governanceFinanceSociologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

abstract Whether or not to adopt and how extensively to use a newly legitimized practice are discrete decisions made by firms undergoing institutional change. The aim of this paper is to identify the distinct effects of economic, social, and political factors on the adoption of performance‐related pay practices and their coverage (i.e. the proportion of employees covered by the practices) by integrating institutional and agency theories. An empirical analysis is performed with a unique sample of Korean firms that experienced the East Asian financial crisis of 1997. The results show that while performance‐related pay adoption was influenced by economic and social factors, performance‐related pay coverage was related to political factors as well as economic and social factors. This finding suggests that while firms adopt performance‐related pay practices in search of legitimacy, they do not blindly imitate such practices but rather proactively adapt them based on economic efficiency considerations. This study makes valuable contributions to research on institutionalism and remuneration by empirically identifying the conditions under which a pay practice adopted for social legitimacy fits or fails to fit the economic needs of the adopters.

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)0.000
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 score0.572
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.025
GPT teacher head0.241
Teacher spread0.216 · 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

Citations45
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Management StudiesSame topicCorporate Finance and GovernanceFrench-language works237,207