Green recruitment and selection: an insight into green patterns
Bibliographic record
Abstract
Purpose Although the role of green recruitment and selection (GRS) has been widely recognised as an important dimension of green human resource management, no study has ever mapped the terrain of GRS and reviewed the literature. The purpose of this paper is to fill this gap while exploring the following questions: How do organisations select candidates in line with their pro-environmental stance? What impact do a company’s corporate environmental sustainability (CES) practices have on attracting pro-environmental job seekers? Design/methodology/approach This paper provides a systematic review of 22 peer-reviewed articles published during the period 2008–2017. The articles were included in the review if they addressed at least one of the two research questions. Findings Some companies choose to apply green criteria when selecting candidates while others do not. In any case, communicating a company’s environmental values and orientation is worth practicing during GRS. Previous studies have identified four mediators (anticipated pride, perceived value fit, expectation of favourable treatment, perceived organisational green reputation/prestige) that intervene between signals of a company’s CES and a job seeker’s perceptions of organisational attractiveness. However, the strength of this effect is influenced by five moderators (pro-environmental attitude, socio-environmental consciousness, desire to have a significant impact through one’s work, environmental-related standard registration, job seeker’s expertise). Originality/value This paper provides the first systematic review of GRS and thus paves the way for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".