The Influence of Informed Consent on Applicant Reactions to Social Media Screening
Bibliographic record
Abstract
The screening of social networking websites is becoming an increasingly common practice among selection professionals. However, academic research has seldom considered applicant reactions to this practice; most studies have examined the efficacy of social media screening on measures of applicant performance. Accordingly, this research seeks to examine the effects of social media screening on an applicant’s attraction to a hiring organization. Moreover, it is proposed herein that organizational disclosure of social media screening - which has yet to be examined in the literature - will serve to influence applicant outcomes on not only organizational attraction, but on their trust in the hiring organization and their affective reactions. Using a 5X2 experimental approach, applicant reactions to a variety of organizational explanations for social media screening - and the pre-screening disclosure of said explanations - were examined. Surprisingly, results indicate that applicants are indifferent towards the specific explanation provided for the organization’s engagement in social media screening; however, disclosure of social media screening was found to have a significant effect on applicant reactions, resulting in higher levels of applicants’ trust in and attraction towards the hiring organization. The importance of this finding regarding applicant reactions to an increasingly prevalent - yet controversial - hiring practice is then 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 | 0.075 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".