The impact of individual values on human resource decision‐making by line managers
Bibliographic record
Abstract
Purpose This paper explores this relationship between the individual values of managers and human resource (HR) decision‐making. Design/methodology/approach Questionnaire data were collected from a total of 340 line managers from both Ireland and Canada. The questionnaire instrument comprises three components: Rokeach's instrumental and terminal values instrument; two HR related decision scenarios; and demographic and human capital data. Findings The results provide modest support for the proposed model that individual values affect HR decision‐making in that capability values were shown to be a significant positive predictor of the importance of health and safety, and peace values were a significant positive predictor of the importance of employment equity. Research limitations/implications The findings emphasise the need to simultaneously examine both individual values and organisational factors as predictors of HR decision‐making. Future work should examine the psychometric use of value instruments. Practical implications The study underlines the fact that managers need to be aware of the fact that their own values influences how they make decisions. Attention to the values concept amongst managers will improve comprehension of the decision‐making process within organizations. Originality/value The value of the paper lies in the fact that the effect of individual values on decision‐making has been under‐researched in the literature.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 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".