Sources of satisfaction with high-potential employee programs
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the perceptions that impact Canadian organizations’ satisfaction with their high-potential (HIPO) identifying practices. More specifically, the paper investigated the perceptual lenses used by HR professionals to view their HIPO identification programs and the elements of such programs that impact satisfaction. Design/methodology/approach – A structural equations modeling technique was used to analyze responses to a national survey (n=219) conducted through a leading Canadian publication for human resource practitioners. Findings – The results reveal that HR professionals form their perceptions of HIPO identification programs on the basis of perceived effectiveness to accurately identify HIPO employees, fairness and motivation. The results further indicate that the degree of formalization of an organization's approach toward identifying HIPOs is the most impactful element for determining satisfaction. Research limitations/implications – First, the study relied on a relatively small sample. Second, the criterion measures used in the study were not continuous. Third, data were collected using self-report questionnaires. Practical implications – The results suggest that organizations should be primarily concerned with adopting a formal, systematic approach when implementing a HIPO identification process. The paper identifies several other elements that organizations should consider in order to maximize their satisfaction with HIPO programs, as well corresponding mediating perceptual lenses. Originality/value – While many organizations regard HIPO programs as essential for their future success, most are not satisfied with their initiatives. This study makes an important contribution to the understanding of the sources of satisfaction with such programs. To the knowledge, this study is the first attempt to understand the factors that determine organizations’ satisfaction with their HIPO identification programs and, therefore, it makes a significant contribution to the literature on developing leadership capability through design and implementation of HIPO programs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".