Interpreting organizational survey results: a critical application of the self-serving bias
Bibliographic record
Abstract
Surveys are used extensively by researchers and practitioners in organizations to measure employee attitudes and assess organizational health. Survey items can reflect a wide range of topics including employee attitudes, perceptions of management, and organizational culture. Surprisingly, the issue of whether employee focused items produce more positive employee responses (vis-à-vis manager or organization focused items) has received little attention. Specifically, there may be self-serving biases in organizational survey responses that may lead to inaccurate diagnosing of organizational problems. We assess the impact of self-serving biases on the pattern of employee responses to organizational surveys. Results from two studies suggest that employees respond more positively to items that are self-focused and less positively to items that are other-focused. Therefore, to the extent that surveys contain both types of items, these biases may influence the diagnosis of organizational problems. In addition, results from the second study suggest that employees glorify themselves for both self-enhancement and social desirability reasons. Implications 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 | 0.574 | 0.781 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".