Personal characteristics and applicants’ perceptions of procedural fairness in a selection context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine a varied set of personal characteristics (i.e. cultural values tied to Confucianism, Big Five personality attributes and test experience) for their combined ability to predict job applicants’ expected and experienced procedural fairness in the context of personnel selection. Design/methodology/approach A total of 324 applicants were surveyed as part of a process to select entry-level positions at a large IT manufacturing company in eastern China. Data were gathered in two waves, such that applicants’ personal characteristics and fairness expectations were obtained prior to their perceptions of procedural fairness, which were collected after the selection interview. Findings Confucian values, neuroticism, conscientiousness and test experience all predicted applicants’ procedural fairness expectations. Only test experience had both direct and indirect effects on procedural justice perceptions. All other effects involving personal characteristics and experience of procedural fairness were mediated by applicants’ fairness expectations. Research limitations/implications The demonstration of the impact of a varied set of personal characteristics on applicants’ perceptions of procedural fairness is consistent with theory-driven models intended to understand and predict these perceptions. The findings suggest, among other considerations, that multinational businesses cannot assume that a standardized approach to selection will be viewed in the same manner by applicants across national contexts. Originality/value The authors show, in an operational employee selection context, how a varied set of personal characteristics can usefully combine to predict applicants’ procedural fairness expectations, as well as their experience of procedural fairness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it