Integrating national culture into the organizational performance feedback theory
Bibliographic record
Abstract
The organizational response to performance feedback is a collective process in which groups of decision-makers face uncertainty about the future when making organizational decisions based on past performance feedback. Culture is an important variable to explain the context of collective decision processes, but it is not well understood by Organizational Performance Feedback Theory (PFT) research. The current internationalization trend of PFT research poses the question if empirical results are comparable across different cultural settings, and whether culture is a general condition of organizational performance feedback. We analyze the role of national culture as a proxy for collective interpretive processes that influence the organizational decision-making process in response to performance feedback, and we resolve some of the unexplained variances in the empirical results. We use a meta-analysis to understand if national culture poses a general condition for the organizational performance feedback process. We analyze the empirical results of 153 PFT studies covering organizations from 16 countries, and we examine the impact of four dimensions of national culture: uncertainty avoidance, performance orientation, future orientation, and institutional collectivism. We demonstrate that national culture is an important concept for PFT development.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".