Communication as a Key Performance Indicator in Employer Branding in the Context of the Social Economy—A Quantitative Study
Bibliographic record
Abstract
Performance measurement refers to the systematic evaluation and analysis of the performance and results of business processes, initiatives, or strategies. This study discusses the crucial role of communication using signaling theory in employer branding in the context of the social economy organization (SEO). The aim is to measure employee satisfaction in concrete terms and to determine the status quo of the communication culture of the organization under investigation in order to develop an employer branding strategy based on the results. The authors use an employee survey as a quantitative research method and limit the data collection to the EU member state of Germany considering the research background. The results provide insights into the specific communication policy in relation to employer branding. The focus here is on (digital) communication. Organizations need to understand how communication strategies directly influence the perception of the employer brand in the social economy. Furthermore, practical implications are derived in order to increase employer attractiveness. Concrete recommendations of action for SEOs should help them be successful in the competition for qualified specialists and talent.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".