Exploring E-waste Management Practices in South African Organisations
Bibliographic record
Abstract
The previous studies have delineated the weakness and drawbacks of e-waste practices in various organisations. However, these studies failed to address major issues relating to effective e-waste management, hence the high rate of obsolescence of electronic equipment continues to grow becoming the fastest growing waste stream in the world. Organisations are paying little attention to the environmental aspects, and there is limited research surrounding e-waste in South African organisations. The primary motivation of the study was to determine the current e-waste practices in the South African organisations to contribute to sustainable e-waste management. The sample was drawn from ten South African organisations from the viewpoint of Information Technology (IT) professionals and managers through semi-structured interviews. The findings of the study revealed that there is a lack of environmental awareness programme in South African organisations, research is not conducted during the procurement process and there is no procurement strategy, limited budget is provided to purchase electronic equipment, hardware’s and software’s are not upgraded to extend the life span of electronic equipment, and data is not managed appropriately, there is lack of recycling capacity, obsolete electronic is not properly disposed of, and there is non-conformance to environmental legislations due to lack of enforcement. The study recommends an increased environmental awareness programme in South African organisations, research to be conducted prior purchasing, develop procurement strategy, provide sufficient budget and purchase environmentally friendly electronic equipment that are less harmful to the environment. The hardware’s and software’s should be upgraded to extend the life span of electronic equipment, and recycling of e-waste should be conducted to reduce and manage e-waste. To some extent, obsolete electronic equipment should be returned to the supplier to ensure safe disposal, and effort should be made to ensure regulatory compliance with the environmental legislations.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".