When Performance Appraisals Fail: Emotion Regulation and the Direction of Organizational Routines
Bibliographic record
Abstract
Abstract Despite their ubiquity, there is much uncertainty as to whether performance appraisals work, and considerable evidence as to their shortfalls. Drawing on the sociological literature on routine dynamics, we examine the embeddedness of the performance appraisal, the role of emotions, exploring what goes on during the process, and how as a routine, appraisals may be diverted onto a trajectory increasingly incompatible with organizational goals. Previous studies have explored how managers may successfully intervene when routines become dysfunctional; we explore when and how this becomes difficult or impossible. We assess how negative emotions introduce a layer of backstage complexity to the appraisal routine. It is based on professional service firms' subsidiaries in a Middle Eastern context, where informal rules and network ties may subvert formal organizational ones. The study identifies distancing, working around, and buffering, as key responses to current and anticipated future negative affective events. It highlights how emotions are not simply antecedents or consequences in the appraisal process; they are experienced throughout the process, accompanying each action, interaction, and event. This study extends routine dynamics and performance appraisal literature by highlighting the emotional dimension's intervening role and examining actors' contending subjectivities. We emphasize how actors shape appraisal routines in diverse and individualistic ways at the micro‐level.
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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 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".