The effect of self-construals on perceptions of organizational events
Bibliographic record
Abstract
This empirical research examines the effect of culture on the way people perceive and assign causes to events in organizations. It explores the idea that attributional biases and errors are moderated by a person’s culture. Results supported proposed hypotheses; they showed that Indonesians, New Zealanders, and Canadians perceived their interdependent self-construal as salient, moderately salient, and least salient, respectively. Furthermore, self-construals moderated and mediated people’s perceptions of organizational events. High-interdependents attributed negative organizational events to factors that are external, less controllable, thus had a more fatalistic outlook compared to Moderate-interdependents and Low-interdependents, respectively. In contrast, Low-interdependents attributed positive organizational events to more internal and stable factors compared to Moderate- and High-interdependents, respectively; they also perceived positive events as being controllable and caused by their own actions compared to High-interdependents. Implications for management practices in multinational organizations are discussed.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".