The Circular Economy and Marketing: A Literature Review
Bibliographic record
Abstract
The focus on the circular economy (CE) is currently gaining momentum. In this paper, we examine how the objectives of the CE significantly overlap with those of the new generation of marketing, which emphasizes customer involvement in product design and responsible consumption. While the marketing function is essential for realizing the CE, there is still a lack of studies examining the intersection of these two critical concepts. Methodically, this paper aims to bridge this knowledge gap by conducting a systematic literature review that explains the CE-marketing nexus. In total, 45 studies were thoroughly analyzed, and findings indicate that the intersection between the CE and marketing spans four main research themes; (1) contribution of green marketing to the CE, (2) remanufacturing marketing, (3) product-service systems, and (4) neuromarketing tools. An agenda for future investigation of the CE and marketing concepts is suggested, followed by a brief conclusion. This review is valuable for scholars and managers, including those striving to have an increased understanding of the relationship between the CE and marketing. How to Cite:Rejeb, A., Rejeb, K., Keogh, J. G. (2022). The Circular Economy and Marketing: A Literature Review. Etikonomi, 21(1), 153-176. https://doi.org/10.15408/etk.v21i1.22216.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".