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Record W4417186550 · doi:10.1002/bse.70387

Are Ecosystems the Missing Link in Circular Transitions? Insights From a Comprehensive Literature Analysis

2025· article· en· W4417186550 on OpenAlexafffund
Aline Gabriela Ferrari, Fabiano Armellini, Daniel Jugend, Davide Pulizzotto, Catherine Beaudry

Bibliographic record

VenueBusiness Strategy and the Environment · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsCircular economyStakeholderLatent Dirichlet allocationEcosystemSystematic reviewBusiness ecosystemKey (lock)Business model

Abstract

fetched live from OpenAlex

ABSTRACT Although recent literature on the circular economy ( CE ) has highlighted the important role of ecosystems, there is still limited understanding of the main themes that characterize circular ecosystems. This study addresses this gap by combining a comprehensive topic modeling analysis employing latent Dirichlet allocation (LDA) with a systematic literature review of 66 articles focused on circular ecosystems. The main results reveal that circular ecosystems have often been analyzed through the lens of (i) circular business models and ecosystem innovation, (ii) CE policies and stakeholder governance, and (iii) implementation and transition to CE . Furthermore, based on this study's findings, topics that have not yet been explored in this area are identified, and a research agenda is suggested. The results of this study also underscore key themes in the relationship between CE and ecosystems and provide managers with guidance on better integrating their businesses into ecosystems aligned with circularity objectives.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.191
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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