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Record W2969482085 · doi:10.1108/jeim-06-2018-0129

Exploring the relationships of the culture of competitiveness and knowledge development to sustainable supply chain management and organizational performance

2019· article· en· W2969482085 on OpenAlexaff
Kamel Fantazy, Syed Awais Ahmad Tipu

Bibliographic record

VenueJournal of Enterprise Information Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsBusinessKnowledge managementStructural equation modelingOriginalitySupply chainOrganizational cultureResource (disambiguation)Resource-based viewSupply chain managementValue (mathematics)Business administrationProcess managementManagementMarketingComputer scienceCompetitive advantageEconomicsSociologyQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to draw upon the resource-based view of the firm to explore how a firm’s resources (assets and capabilities) such as culture of competitiveness (CC) and knowledge development (KD) relate to sustainable supply chain management (SSCM) and organizational performance (OP). Design/methodology/approach Data were collected from 242 supply chain and logistics managers in Pakistan and a structured equation modeling approach was used. Findings The results of the study provide support for the proposed hypotheses and indicate that CC and KD are positively related to SSCM and OP. This points out that the organizations in Pakistan are likely to emphasize CC and KD to achieve OP. However, the positive but weak association of CC and KD with SSCM highlights that the organizations in Pakistan show less concern for SSCM. Originality/value The literature did not reveal any study which examined the relationships of the CC and KD to SSCM and OP in developing countries. The present study aims to address this gap in the literature.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.484

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 communication0.0000.002
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 head0.013
GPT teacher head0.192
Teacher spread0.179 · 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

Citations54
Published2019
Admission routes1
Has abstractyes

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