Leveraging social media to enhance recruitment effectiveness
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how firms can use social media such as Facebook to recruit top job prospects. Design/methodology/approach – In the context of a fictitious event presumably sponsored by a potential employer, a sample of university students became members of a new private and secret Facebook user group dedicated to this event for a period of four days. They were exposed to event sponsorship activation messages varying systematically with respect to the mode of processing (i.e. passive or active) and their focus (i.e. the brand or the event). Findings – The results show that their expectations as regards the salary that they would require to become employees were higher in the active mode of processing. Also, their attitude toward the sponsor as an employer was more favorable when the activation messages focussed on the brand rather than on the event. In addition, further analyses showed that the effects of message focus and mode of processing on the attitudinal responses toward the sponsoring employers were mediated by the degree of elaboration and richness of social interactions of the Facebook group's members as well as their attitude toward the activation messages. Practical implications – Managers seeking to gain a recruiting edge through their social media presence should use online messages that stimulate more active processing and that have high entertainment value since this leads to more favorable responses toward the employer. These messages should insist more on the brand than on the event that is sponsored. Originality/value – This study is the first study to foray into the usage of social networking sites for recruitment purposes. It represents one of the few research efforts to monitor the interactions of users in a social media platform by means of a controlled experiment performed in situ through the creation of an ad hoc Facebook group.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".