Reframing the performance management system: a conversations perspective
Bibliographic record
Abstract
Purpose To explore human resource (HR) practitioner perspectives of the effectiveness, challenges, and aspirations of the performance management (PM) system to inform future directions for PM design and success. Design/methodology/approach Interviews with 53 HR practitioners from a cross-section of organisations operating in the United Kingdom, Canada and New Zealand. Findings Practitioner's discussed the criticality of effective conversations across all elements of the PM system. Using an interpretive approach, and through a lens of social exchange theory (SET), we used their voice to develop a conversations-based PM model. This model centres on effective performance conversations through the design and implementation of the PM system. It includes four enablers and five environmental elements. The enablers (aligned goals, frequent feedback, skills development, and formality) depend on skilled interactions and conversations, and the organisational environmental elements (design, development function, buy-in, culture, and linkage to other systems) are enhanced when effective conversations take place. Practical implications Practitioners can use the conversations model to help shape the way they design and implement PM systems, that place emphasis on upskilling participants to engage in both formal and informal honest conversations to build competency in the enablers and assess organisational readiness in terms of the environmental elements. Originality/value By listening to the under-utilised voice of the HR practitioner, and through a lens of SET, we developed a PM model which emphasises reciprocity and relationship building as key tenets of the PM system. While past research recognises the importance of effective conversations for PM implementation, it has largely silent been about the role of conversations in system design. Our model centres these conversations, presenting enablers and environmental elements to facilitate their core position within effective PM.
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".