Organizational attractiveness: Targeting prospective employers on social networking sites
Bibliographic record
Abstract
BACKGROUND: Many business organizations have integrated the use of professional social networking sites into their HR practices in order to communicate with and attract qualified candidates as part of their talent acquisition strategy. OBJECTIVE: The aim of this research is to explore some social and behavioral signals on social networking sites that enhance organizational attractiveness. Grounded in the signaling theory, this paper fills the research gap by investigating new types of signals on public professional social networking sites that can affect organizational attractiveness as an employer. METHODS: In this research, a quantitative research methodology was used. The sample consists of 288 job applicants using social networking sites in Canada. RESULTS: The results highlighted the importance of social and behavioral factors that play a significant role in enhancing organizational attractiveness on professional social networking sites. CONCLUSIONS: The results provide insights and practical suggestions for managers who decide to integrate social networking sites into their practices. Additionally, the findings of this research help the managers to better understand the factors that have an impact on job applicants’ choices of their future job and employer.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".