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Record W4390298761 · doi:10.1002/bse.3661

Exploring sustainable development goals adoption in supply chain management: A typology of coexisting institutional logics

2023· article· en· W4390298761 on OpenAlexaff
Liliane Carmagnac, Minelle E. Silva, Morgane M.C. Fritz

Bibliographic record

VenueBusiness Strategy and the Environment · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTypologySustainabilityBusinessSustainable developmentSupply chainSupply chain managementProcess managementIndustrial organizationMarketingSociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Despite growing interest in the 17 United Nations (UN) sustainable development goals (SDGs), companies still struggle with how to implement them as part of their sustainability policies. Hence, we explore how the SDGs are adopted in supply chain management (SCM) using institutional logic as a theoretical lens. Based on a case study, we analyse 10 interviews conducted with managers and suppliers of a multinational cosmetics company recognised for its sustainability engagement. Our findings show that while the strategic level embraces the SDG framework, supply management processes are exclusively evident at the operational level. This becomes even more salient in the analysis of sustainability and commercial coexisting logics. Although sustainability logic concerns compliance with sustainability‐related regulations and external pressures, commercial logic demonstrates the effective engagement of strategic decisions in favor of profitability. Our results reveal nuances in the existing gap between the SDGs and SCM, illustrated through a typology of coexisting logics at the strategic and operational levels, adding value to the SCM literature. This paper questions the feasibility of implementing the UN SDGs along a supply chain, which opens the door for future research and new practical insights relevant to companies' daily operations.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.048
GPT teacher head0.210
Teacher spread0.162 · 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 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

Citations21
Published2023
Admission routes1
Has abstractyes

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