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Record W2910694652 · doi:10.3390/su11020390

Assessing the Potential of Sustainable Value Chains in the Collaborative Economy

2019· article· en· W2910694652 on OpenAlexaff
Myriam Ertz, Emine Sarigöllü

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsMcGill UniversityHEC MontréalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsArchetypeSustainabilitySustainable ValueValue chainSharing economyIndustrial organizationBusiness modelValue (mathematics)BusinessMarketingCircular economyProfit (economics)EconomicsKnowledge managementMicroeconomicsSupply chainComputer science

Abstract

fetched live from OpenAlex

The current business paradigm entails a narrow, profit-centered and managerially-focused nature. This article proposes that the study of the collaborative economy necessitates an inevitable shift in the conventional business paradigm and suggests that the institutional school of marketing thought, in general, and the electric theory of marketing, in particular, offers a useful theoretical framework for investigating the theoretical impact of the collaborative economy on the value chain. Uber is used as an illustrative case, on which the electric theory of marketing is applied, to demonstrate how the archetype of the collaborative economy theoretically impacts the value chain and contributes to sustainability in the value chain in the transportation services industry. The study provides further insights in the form of suggestions and propositions for ensuring sustainability in the value chain of collaborative systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0070.013
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.244
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations12
Published2019
Admission routes1
Has abstractyes

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