Compensation Plan Implementation and Change: Consequences for Individuals, Teams, and Firms
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".