An Integrative Model for Resilience Through Circular Business Models: Insights Using a Multilevel Perspective
Bibliographic record
Abstract
ABSTRACT This study investigates how circular business models (CBMs) can lead to resilience. Despite the increasing number of CBM studies, they have not fully addressed the need for resilience. As such, we employ a multilevel perspective to reveal the existing and potential relationships between CBMs and resilience under the sustainability umbrella. A two‐stage methodological approach was adopted by reviewing 125 published studies to unpack the way these constructs are interconnected, and interviewing experts to have an up‐to‐date perspective about CBMs and resilience. Data were analyzed using content analysis and organized through the CBM canvas to illustrate our results on value creation, proposition, delivery, capture, and resilience capabilities. The findings show that scholars often focus on narrow elements of environmental or economic sustainability and neglect the effective impact of circularity in various contexts. In parallel, experts reinforce how CBMs are context dependent and must urgently tackle climate change. An integrative model based on our analysis was developed to represent how CBMs can be aligned with resilience across multiple levels. This model extends our theoretical understanding of ways to fit resilience into CBMs and can help practitioners identify pathways for moving forward to achieve a circular economy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".