Employer Branding: Creating a Sustainable Recruitment Plan in Large Corporates
Bibliographic record
Abstract
The aim of this study is to attract talented employees by using non-traditional recruitment methods, such as online professional business networks. These methods should encourage potential employees to apply and join these companies. It also allows the company to position its image in its potential customers’ minds. Hence, this research contributes to how to position an image in potential employee’s minds and motivate them to be part of the company. The main objective of this study is to investigate how to maintain a continuous demand from potential employees; accomplished by providing an outline of a sustainable recruitment plan to be applied in the larger Palestinian corporates. A questionnaire was distributed at employees and their HR managers at four large-scale corporations in West Bank, Palestine. The proposed models were analyzed on the basis of 100 responses related to four well-known large Palestinian corporates. The results indicate that 78.3% of the sample have a recruitment plan in their corporations. Moreover, most of the sample, over 50%, have job satisfaction. which can enhance and help corporations in building a good reputation. It also exploits its internal employees as a strategic marketing tool to build their real, good, and attractive employer branding. The study recommends that companies seriously consider their employees in their marketing strategy, similar to how they market their products and services. To accomplish this, they need to build a long-term relationship with employees in a way that affects the general profit of the corporate and assists the corporate to create and maintain the employer branding.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".