Why Are Job Seekers Attracted by Corporate Social Performance? Experimental and Field Tests of Three Signal-Based Mechanisms
Bibliographic record
Abstract
Research on employee recruitment has shown that an organization's corporate social performance (CSP) affects its attractiveness as an employer, but the underlying mechanisms and processes through which this occurs are poorly understood. We propose that job seekers receive signals from CSP that inform three signal-based mechanisms that ultimately affect organizational attractiveness: job seekers' anticipated pride from being affiliated with the organization, their perceived value fit with the organization, and their expectations about how the organization treats its employees. We hypothesized that these signal-based mechanisms mediate the relationships between CSP and organizational attractiveness, focusing on two aspects of CSP: an organization's community involvement and pro-environmental practices. In an experiment (n = 180), we manipulated CSP via a company's web pages. In a field study (n = 171), we measured CSP content in the recruitment materials used by organizations at a job fair and job seekers' perceptions of the organizations' CSP. Results provided support for the signal-based mechanisms, and we discuss the implications for theory, future research, and practice.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".