Antecedents for greening the workforce: implications for green human resource management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Green human resource management (GHRM) is an arising issue for the tannery industry in the context of developing economies. As the tannery industry can be seen as one of the highest polluting industries on earth, it becomes imperative for the industry to implement GHRM practices for greening the workforce. In this context, the purpose of this paper is to focus on antecedents that will support the implementation of GHRM practices in the tannery industry supply chain. Design/methodology/approach In this study, an expanded literature review was organized to establish antecedents for implementing GHRM practices. The total interpretive structural modeling (TISM) technique is employed to explore interactions among the identified antecedents. Furthermore, Matriced Impact Croises Multiplication Applique analysis was conducted for determining the driving-dependence power of each antecedent. Findings The results revealed that “green selection facility,” “green recruiting facility,” “green organizational culture,” “green purchasing,” “green strategy towards ES,” “regulatory forces towards ES” and “top management commitment towards greening the workforce” are the key antecedents for the exercise of GHRM practices in the tannery industry. Practical implications The proposed model might support decision makers to understand the interactions among the antecedents of GHRM practices. This model will help managers to understand the impact of one antecedent on another prior to the implementation of GHRM practices in the tannery industry. Originality/value In this study, the author(s) propose a new version of the interpretive structural modeling approach (ISM), named the TISM technique, for determining the contextual interactions between GHRM initiative antecedents that are very new in the existing literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it