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Compensation Plan Implementation and Change: Consequences for Individuals, Teams, and Firms

2018· article· en· W2859807137 on OpenAlexaboutno aff
Spenser Essman, Anthony J. Nyberg, Nina Gupta, Jason D. Shaw

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsRemunerationCertificationCompensation (psychology)ManagementPolitical scienceSociologyLibrary sciencePsychologyComputer scienceEconomicsLawSocial psychology

Abstract

fetched live from OpenAlex

Implementing compensation plans is a vital human resource practice that affects individuals, teams, and organizations. However, compensation research remains neglected in management research. Specifically, little is known about the effects of implementing new compensation plans or changing existing plans. Understanding the response to changes in compensation is especially important under dynamic economic conditions. The papers in this symposium analyze rich field data and contribute to theory by examining the implementation of, and changes to, compensation plans and the effects on individuals, teams, and organizations. Impact of Changing Skill-based Pay Certification Criteria on Skill Proficiency Presenter: Eric Alan Surface; ALPS Insights Presenter: James Kemp Ellington; Appalachian State U. Presenter: Samantha A. Conroy; Colorado State U. Presenter: Reanna Harman; ALPS Solutions Presenter: Don Drewes; North Carolina State U. Presenter: Lauren Brandt; ALPS Solutions Presenter: Elisabeth Dezern; ALPS Insights Identity Work in Resolving the Paradox of Compensation System Implementation Presenter: Aino Tenhiälä; IE Business School Presenter: Saku Mantere; McGill U. Individual and Firm Response to the Remuneration Transparency Act in Germany Presenter: Spenser Essman; Darla Moore School of Business, U. of South Carolina Presenter: Anthony J. Nyberg; U. of South Carolina Presenter: Ingo Weller; LMU Munich Presenter: Julia Ebert; Ludwig Maximilian U. of Munich (LMU) Presenter: Lena Göbel; Ludwig Maximilian U. of Munich (LMU) Are Team Rewards Better than Other Pay Plans? A Meta-Analytic Investigation Presenter: Pingshu Li; UTRGV Presenter: Keshab Acharya; the U. of texas rio grande valley Presenter: Eduardo Millet; U. of Texas Rio Grande Valley Presenter: James P Guthrie; U. of Kansas Workplace Consequences of Competitive vs. Egalitarian Strategic Compensation Plans Presenter: Mahmut Bayazit; Sabanci U. Presenter: Dennis George Ma; U. of British Columbia Presenter: Danielle Van Jaarsveld; U. of British Columbia

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)0.022
metaresearch head score (Gemma)0.097
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0050.005
Scholarly communication0.0100.005
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.098
GPT teacher head0.388
Teacher spread0.290 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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