Managers' justice perceptions of high potential identification practices
Bibliographic record
Abstract
Purpose The purpose of the present study was to describe the high potential identification practices of Canadian organizations and to assess elements of these practices as they relate to managers' perceptions of organizational justice. Design/methodology/approach The study reviewed the literature on high potential identification practices and organizational justice to develop a survey for managers attending a leadership conference. Distributive and procedural justice was regressed against the elements of these programs (e.g. the extent of manager input into the program, the openness of communications) to determine the impact of program elements on justice outcomes. Findings The paper reveals that approximately one‐third (38 percent) of companies reported having a high potential identification program. High potential was most often defined in specific organizational terms based on competencies. Typically, information used to identify these individuals was based on: personal experience with the person, performance appraisals and past performance or results. Hierarchical linear modeling analyses (n=123) indicated that high potential identification programs containing manager input, open communication and formal program evaluation significantly predicted procedural justice. None of the predictions for distributive justice were significant. Originality/value This study is the first to empirically investigate the impact of high potential identification practices on managers' perceptions of organizational justice in North America. Manager's justice perceptions reflect an important criterion to evaluate high potential identification programs. The current study found that manager's perceptions of procedural justice were higher when they had more input into the development of the program, when the communication strategy was more open, and the program was evaluated. Despite these important elements, many organizations do not incorporate them into their programs, which have implications for their success.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".