Impact of Green Human Resource Management Practices on Environmental Performance of Indian Banking Sector: An Empirical Study
Bibliographic record
Abstract
The ever increasing serious consequences of global warming and climate change have made the environmental performance, a top concern for all organisations.Since anthropogenic activities are the main cause behind the environmental degradation, it is in the domain of human beings for taking rectificatory steps to save the situation.In this context, Green Human Resource Management (GHRM) practices hold hope for improving environmental performance through reduction of Green House Gas (GHG) emission and efficient use of natural resources and energy.The aim of this paper is to examine the impact of GHRM practices of Green Recruitment and Training (GRT) and Green Performance and Reward (GPR) on environmental performance in the Indian Banking sector.An empirical analysis has been done in State Bank of India (SBI) and HDFC Bank, the largest and leading Indian banks in public and private sector respectively.Hypothesis has been developed using Ability, Motivation and Opportunity(AMO) theory, Natural Resource Based View (NRBV) theory and Social Exchange theory (SET).With Structural Equation Modelling (SEM) technique,the data analysis is done using the statistical tool SMART PLS4.The results reveal that GRT and GPR can have significant impact on environmental performance via the mediation of Organisational Citizenship Behaviour for Environment (OCBE).The Importance and Performance Matrix Analysis (IPMA) also shows that OCBE as a mediator is the best performing construct in influencing the environmental performance in both the organisations.The study has practical implication for practicing bankers and policy makers to effectively mould GHRM practices towards better environmental performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".