Self‐Assessment versus Self‐Improvement Motives: How Does Social Reference Group Selection Influence Organizational Responses to Performance Feedback?
Bibliographic record
Abstract
Abstract The performance feedback theory (PFT) proposes that organizations compare their performance to other organizations (i.e. their social reference group) and initiate responses based on this comparison. While social comparison represents a core element of the PFT, it is not well understood how organizations select social reference groups and how this selection may affect organizational responses (e.g. risk‐taking, change, innovation). We propose that the motives that organizations use to select their social reference groups impact their responses to performance feedback. Our meta‐analysis of 99 empirical PFT studies focuses on two motives underlying the selection of social reference groups for performance feedback: self‐assessment and self‐improvement. While self‐assessment through comparison requires the selection of a relevant set of referent organizations, self‐improvement relies on the selection of the highest performing referent organizations. Our results show that organizational responses to performance feedback differ depending on which motive‐based reference group is selected for comparison. These differences are more evident when performance is above aspirations. This finding has important implications for PFT researchers to predict organizational responses more precisely.
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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 | 0.041 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".