High potential programs and employee outcomes
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how and under what conditions employees’ participation in high potential (HiPo) programs leads to various employee outcomes (i.e. affective commitment, job satisfaction, organizational citizenship behaviors (OCBs), and turnover intent). Design/methodology/approach Data were collected by a cross-sectional survey among 242 employees who had HiPo programs in their current organizations. Findings Findings provided support for the mediating role of commitment-focused HiPo attributions in the relationships between HiPo program participation and employee outcomes (affective commitment, job satisfaction, OCBs, and turnover intent). The results also demonstrated significant interaction effects of HiPo program participation and organizational trust on commitment-focused attributions. Additionally, the results provided support for several mediated-moderated models. Research limitations/implications This study opened the “black box” by examining the processes through which talent management (TM) shapes employee attitudes and behaviors, and demonstrated that these relationships are not necessarily direct. Practical implications To ensure employees’ career success, organizations need to build trustworthy relationships with their employees, and must consider the processes related to the talent identification, as well as the messages this identification communicates to employees about their contributions. Originality/value This study is the first to examine employees’ attributions about their participation in HiPo programs. Further, this study is also the first to empirically investigate the role of employees’ perceptions of organizational trust in the context of TM.
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 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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".