Performance Management in Practice: The Power of Words in the Words of HR Practitioners
Bibliographic record
Abstract
Some 65 years ago, Thorndike (1949) highlighted four criteria for effective performance management (PM) systems: reliability, validity, freedom from bias and practicality. While the literature has a rich and deep history concerning the first three criteria, limited scholarly work has examined practicality, and even less has examined the perspective of the human resource (HR) practitioner. We believe this void is problematic as these HR practitioners often design and implement PM systems. As such, they have a unique and important perspective concerning PM. In this study, we interviewed 45 people involved in PM design and implementation from Canada, the United Kingdom and New Zealand, in order to gain insights concerning what they felt constituted effective PM. Overall, we noted that the effectiveness criteria highlighted by these practitioners did not relate to the psychometric criteria that have dominated the scholarly HR literature. Rather, across the three countries, we found that HR practitioners focused on practical issues related to organizational members being able to engage in effective conversations, whether formal or informal, and that such conversations were seen as the basis of an effective PM system. Underpinning this was the need to create buy-in across the organization to enable these conversations to occur, and the need to set effective goals for these conversations to be useful. However, the reality in which these practitioners worked did not match this ideal “effective conversation” state. We make some suggestions, based upon our HR practitioners’ experience, to rectify this gap.
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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 | 0.078 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.015 | 0.121 |
| Scholarly communication | 0.040 | 0.051 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".