How Talent Identification Influences Perceptions of Organizational Justice and Basic Psychological Needs: A Self-Determination Theory Approach
Bibliographic record
Abstract
This study aims to explore the impact of talent identification practices on employees' psychological needs and to examine the mediating role of distributive justice/injustice between talent identification and psychological needs. Additionally, it investigates procedural justice/injustice as a moderating variable in this mediation. A cross-sectional sample (n=124) with clinical vignettes was used to test the hypotheses through moderated mediation analysis. The findings reveal three key insights. First, talent identification significantly correlates with psychological needs. High-potential individuals reported greater satisfaction of their needs for autonomy, competence, and relatedness compared to regular employees, who reported higher frustration levels. Second, high potentials perceived greater distributive justice, correlating with increased psychological need satisfaction. Conversely, regular employees perceived higher distributive injustice, leading to greater psychological need frustration. Third, procedural justice/injustice did not significantly moderate the mediation. However, procedural justice/injustice was significantly related to psychological needs, independent of distributive justice/injustice. Our research makes a vital addition to the human resource management (HRM) field by providing quantitative empirical analysis of talent identification where prior work has been largely conceptual or qualitative. Given the current labor market's supply-demand imbalance, understanding these dynamics is increasingly critical.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".