Insights and dynamics of circular business model in developing countries' context: The empirical analysis of the returnable glass bottles process
Bibliographic record
Abstract
Abstract Despite the growing understanding that circular business models (CBMs) play a pivotal role in facilitating the transition from a linear to a circular economy, there is a lack of relevant literature on how CBMs can be implemented in businesses in developing countries. This study addresses this significant gap in the literature by revealing the insights and dynamics of the implementation of a CBM in a typical developing economy—Nigeria. A notable business model adopted by breweries and beverage companies in Nigeria—a returnable glass bottle process—was investigated through an in‐depth exploration of six companies in a qualitative case study that involves collecting data through interviews, exploratory field observation, and documented evidence (literature). The study generated empirical‐based evidence on how CBM can be implemented in a business value chain where formal and informal actors co‐exist and interact. It also discloses several barriers and enablers associated with CBM implementation in the context of developing economies. Collaboration, social inclusiveness, waste management, durable product design, and cost reductions are some of the enablers identified in the study. The key barriers are largely external and conspicuously linked to the socio‐economic disadvantages peculiar to developing economies such as the absence of effective legislature, lack of infrastructure, lack of technological innovation, unavailability of finance, and the emergence of large retail stores that operate on a disruptive business model. Finally, the current research provides practical suggestions and recommendations for the appropriate designing and transitioning of CBMs in developing countries' context.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".