Spontaneous trait inferences and organisational actions: The formation of organisation personality perceptions.
Bibliographic record
Abstract
Organisation personality perceptions, or the attribution of human personality characteristics to an organisation, have been found to affect organisational attraction, job pursuit intentions, and organisational reputation. Although the presence and potency of these attributions have been established, little is known about the manner in which these attributions come about, particularly whether the process is consistent with personality attributions made about human targets. In the current paper, we extend previous work by investigating the underlying social-cognitive mechanism by which organisation personality perceptions are formed. Specifically, we tested the proposition that organisation personality perceptions are spontaneously inferred in a manner that is functionally isomorphic with individual personality perceptions. Study I used a cued-recall paradigm, with results indicating that implied trait words improved recall for both individual and organisational actors. Study 2 extended these findings using a lexical decision paradigm; results showed improved performance when making a lexical decision about trait words regardless of whether the actor in a behaviour presented just prior was an individual or an organisation. The results are discussed in terms of their theoretical and practical implications.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".