Reimagining reward management: An exploration of total reward perspectives and their impact on employee retention and motivation
Bibliographic record
Abstract
This paper, "Reimagining Reward Management: An Exploration of Total Reward Perspectives and Their Impact on Employee Retention and Motivation," investigates the comprehensive construct of 'total reward' in human resources management. We examine the various facets of total reward, including compensation and benefits as safety and security needs, health and well-being, esteem recognition, and self-actualization opportunities. The study underscores the necessity for organizations to continuously review their compensation and benefits policies to ensure pay equity, a critical factor in employee motivation and retention. Drawing on theories from Armstrong & Taylor (2020), Milkovich, Newman, & Gerhart (2020), and Maslow (1943), we present a holistic approach to reward management, arguing that an integrated strategy significantly contributes to an organization's overall success. The findings are expected to provide fresh insights into the role of reward management, highlighting its importance in today's competitive business environment. Future research directions include quantifying the impacts of total reward strategies on employee performance and organizational outcomes. Key words: Reward management, Rewards, Employee retention, Motivation, Compensation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".