Exploring a Sustainable Business Model (SBM) for Electronic Waste (e-Waste) Management in York Region, Ontario
Bibliographic record
Abstract
Life without electronic devices, while not impossible, would be difficult at least when trying to communicate. This dependency is one of the multiple variables leading to the fast-growing e-waste generation. Also, the lack of e-waste consumption and management synchronization leads to a flow of toxic substances, increasing the environmental and human risks. Conversely, the recycling of valuable substances of the electronics produces a significant source for re-manufactured products and protecting the natural resources while creating a wide range of business opportunities. The main objective of this research is to investigate the specifications and characteristics of a sustainable business model (SBM) for e-waste management through studying the stakeholders’ perspectives. In the SBM that is visualized by a Sustainable Business Model Canvas (SBMC) and its eleven components, the environment and society drivers are considered as important as economic drivers. However, the experimental studies on operationalizing sustainability in BMs have not been considered as they deserve. Therefore, this study, with its exploratory and interpretative nature, employed an abductive research strategy combined with a case study method to investigate the stakeholders’ perspectives. The semi-structured interviews were the primary source of data. The York Region, Ontario, CA was selected as the case study. The recorded interviews were transcribed through a qualitative content analysis method. The research findings reveal the matrices, graphs, mind maps, and seven mental models from the three major stakeholder groups. Then, the eight emerged themes were discussed thoroughly in response to the research questions. Consequently, the three significant contributions of this research when answering the research questions include: (1) A mental model consisting of the common opinions of three stakeholder groups (Government, Community, and Industry) that characterizes a consistent but incomprehensive SBM for e-waste management of York Region (YR) ; (2) Dual stakeholder group mental models that identify the gaps among stakeholders’ understandings about a SBM-YR-e-waste management system that highlight the conflicts or defects for future design; and (3) An integrated SBMC (I-SBMC) that may meet the sustainability consideration in the SBM, particularly for waste and e-waste management systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".