Outcomes of talent management: the role of perceived equity
Bibliographic record
Abstract
Purpose To date, the effects of two approaches – inclusive and exclusive – to talent management (TM) on employee outcomes are largely unexplored. This paper explores the role of perceived equity and theoretically examines the process through which these TM programs impact employee outcomes. Design/methodology/approach This paper draws on the job demands-resources model and equity theory and proposes a typology of employee outcomes in the context of different approaches to TM. Findings Based on the theoretical framework, the paper argues that in the context of both inclusive and exclusive TM, perceived equity is a valuable resource that motivates employees and results in favourable outcomes. Research limitations/implications Future empirical studies should test the propositions put forth in this paper. The multilevel research design would allow for an in-depth analysis of organisational contexts, and qualitative studies using in-depth interviews can provide greater insights into employees' experiences and perspectives of TM programs. Practical implications The paper presents implications for managers and human resource (HR) and TM professionals regarding how to get the most out of their TM programs. These implications are important since employee equity perceptions can influence the effectiveness of TM programs. Originality/value In this paper, the authors add to the literature by examining the role of employee equity perceptions in the context of inclusive and exclusive TM and to highlight how perceived (in)equity could lead to negative consequences, even among high potential (HiPo) employees.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".