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Record W2030327658 · doi:10.1108/01443571011094253

The impact of supply chain structure on the use of supplier socially responsible practices

2010· article· en· W2030327658 on OpenAlexaffabout
Amrou Awaysheh, Robert D. Klassen

Bibliographic record

VenueInternational Journal of Operations & Production Management · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsWestern University
Fundersnot available
KeywordsSupply chainBusinessSupply chain managementConceptualizationTransparency (behavior)MarketingIndustrial organizationCorporate social responsibilitySupplier relationship managementUpstream (networking)Process managementPublic relationsComputer science

Abstract

fetched live from OpenAlex

Purpose This paper seeks to explore the integration of social issues in the management of supply chains from an operations management perspective. Further, this research aims to develop a set of scales to measure multiple dimensions of supplier socially responsible practices. Finally, the paper examines the importance of three dimensions of supply chain structure, namely transparency, dependency and distance, for the adoption of these socially responsible practices. Design/methodology/approach Drawing on literature from several theoretical streams, current best‐practice in leading firms and emerging international standards, four dimensions of supplier socially responsible practices were identified. Also, a multi‐dimensional conceptualization of supply chain structure, including transparency, dependency and distance, was synthesized from earlier research. Using this conceptual development, a large‐scale survey of plant managers in three industries in Canada provided an empirical basis for validating these constructs, and then assessing the relationships between structure and practices. Findings Multi‐item scales for each of the four dimensions of supplier socially responsible practices were validated empirically: supplier human rights; supplier labour practices; supplier codes of conduct; and supplier social audits. Increased transparency, as reflected in greater product visibility by the end‐consumer was related to increased use of supplier human rights, which in turn can help to protect a firm's brands. Organizational distance, as measured by the total length of the supply chain (number of tiers in the supply chain), was related to increased use of multiple supplier socially responsible practices. Finally, as the plant was positioned further upstream in the supply chain, managers reported increased use of supplier codes of conduct. Practical implications As senior managers extend, redesign or restructure their supply chains, the extent to which social issues must be monitored and managed changes. The four categories of supplier socially responsible practices identified help managers characterize their firm's approach to managing social issues. Furthermore, managers must more actively manage the development of supplier socially responsible practices in their firms when the supply chain has more firms; and when brands have stronger recognition in the marketplace. Originality/value The paper makes three contributions to the extant literature. First, the construct of social issues is defined and framed within the broader debate on sustainable development and stakeholder management. Second, social practices are delineated for supply chain management, and a set of scales is empirically validated for assessing the degree of development of supplier socially responsible practices. Finally, the link between supply chain structure and the adoption of supplier socially responsible practices is examined. This last contribution provides a basis for understanding, so that managers can extend and reshape current views about how social issues must be managed.

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)0.011
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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 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

Citations543
Published2010
Admission routes2
Has abstractyes

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