Virtual Hiring: An Effective Green Human Resource Management Practice
Bibliographic record
Abstract
Attracting high-quality employees is a critical human resource concern in the 'battle for talent. Virtual Hiring by the organization is a step towards sustainability by going paperless. Greening the activities involved in getting people into an organization is covered under the Green HRM. This Research paper attempts to establish an Integrated Model of the virtual Hiring mode, its Predictor and Outcome variables. To evaluate the relation between Perceived Usefulness (PU) and Perceived ease of use (PEU) over the virtual mode of Hiring (VMH) and the further impact of the Virtual Mode of hiring as Green HR Practice on Cost-effectiveness, Geographical Outreach and Environment/ Health benefits. Research Methodology- The study employed an adapted questionnaire to gather data from 266 respondents and used Partial Least Square (PLS) Structural Equational Modelling in SmartPLS software version 3.3.2 to conduct empirical analysis. To sum up, this study developed a new model using some variables of Technology Acceptance Model (TAM) to investigate the mechanism by which the Virtual mode of Hiring as Green HR practice impacts the outcome variables of this study. There found the significant positive impact of virtual Hiring on Cost Effectiveness, Geographical Outreach and Environment/ Health Benefits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".