Marketing meets human resources : a study of employer branding practices in Canada
Bibliographic record
Abstract
The purpose of this study was to find out the current Employer Branding Practices being used by a sample of companies in Canada. The goal was to find out how companies were creating awareness of their employer brands, how they influenced their brand consideration and feelings and judgements about that brand; and how it all came together in reflecting the overall employer branding strategy. This was achieved by interviewing the key Human Resource influencers in fifteen companies in Canada who were regarded by external awarding bodies as great employers. \nThe results of the interviews with a sample of companies in Canada revealed that the employer branding model was split into two separate parts: an internal model and an external model. Internally, the sample companies indeed had very well designed and coordinated strategies for managing and ensuring that their current employees were aware of and considered the attributes of their employer brand, had positive feelings and judgements about their employers. Overall the internal employer branding strategy although not specifically referred to by that name achieved the ultimate goal of creating brand resonance among current employees. However, noticeably absent was a coordinated effort to project that positive employer brand externally. There was very little collaboration between the Human Resources departments and the Marketing departments to create awareness and consideration of the employer brand among potential applicants. Companies instead heavily relied on their current networks, employees, clients, recruiters and the community to project and sell their employer brand externally. \nIt is therefore recommended that companies take control of managing their employer brand externally just as they do internally. A model combining the various aspects of the internal employer branding strategy that can be used to influence potential applicants’ awareness and consideration of an employer during their job search has been proposed. In order for this model to be successful, Human Resources will have to work with Marketing to devise the best way to accurately and consistently project the internal employer brand image externally using one continuous model.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.029 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".