Exploring Challenges and Solutions in Performing Employee Ecological Behaviour for a Sustainable Workplace
Bibliographic record
Abstract
Organisations are a fundamental part of challenges and solutions to climate change issues. Therefore, the micro and macro factors influencing employee ecological behaviour (EEB) are a rising interest among researchers. The contemporary concept of EEB has been embraced by many organisations and attracted scholars’ attention worldwide. Nevertheless, studies that explored challenges and solutions for performing EEB at the workplace are scarce. This study explored challenges and solutions in performing EEB at the workplace and focused on qualitative research methodology. The researchers interviewed 24 academicians from five leading green research Malaysian universities. Valuable qualitative data and numerous challenges such as high costs of practising, lack of infrastructure, top management support, environmental attitude, green mindfulness, enforcement, and monitoring were identified as challenges in applying EEB from the interviews. Stringent rules and regulations, monitoring, training programmes, and monetary incentives might be efficient solutions to apply ecological behaviour at workplaces, specifically universities. In conclusion, this study has discovered the challenges and solutions in implementing EEB for a sustainable workplace by interviewing academicians from different departments of selected Malaysian higher educational institutes. Also, poor infrastructure, high cost, and the lack of top management support, environmental attitude, green mindfulness, enforcement, and monitoring were identified as the primary challenges in performing EEB. Additionally, the research also discovered significant suggestions to resolve the challenges when implementing EEB at the workplace, such as strict rules and regulations, training programmes, incentives, monitoring, and communicating change and campaigns. Therefore, the stakeholders related to the industry should be concerned with the challenges identified when applying EEB at the workplace to apply the solutions generated from the study.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".