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Record W4394886343 · doi:10.5267/j.uscm.2024.3.008

Investigating the role of supply chain management on sustainable performance and dynamic capabilities: An empirical study on logistic organization

2024· article· en· W4394886343 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessLikert scaleSupply chain managementSupply chainSustainabilitySample (material)Empirical researchDynamic capabilitiesMarketingSimple random sampleIndustrial organizationProcess managementOperations managementKnowledge managementComputer scienceEconomicsPsychology

Abstract

fetched live from OpenAlex

This research aims to investigate the effect of supply chain management (SCM) on sustainability performance (SP), the effect of dynamic capabilities (DC) on sustainability performance and finally the effect of SCM on DC. The study uses a quantitative method with a questionnaire approach to investigate the relationship between endogenous and exogenous variables using the Likert scale. The respondents for this research were 680 logistics company owners in Indonesia determined using a simple random sampling method. The results show that SCM had a positive and significant relationship with SP. DC also had a positive and significant relationship with SP and recommends that company owners create policies to increase dynamic capabilities to improve company performance. Finally, DC in our survey had a positive and significant relationship to SP and strengthened the findings of previous findings. Moreover, SCM had a positive and significant relationship with DC, which recommends company owners make policies to improve SCM to increase DC. This research provides input to organizational owners to implement supply chain management, and dynamic capabilities to improve company performance and competitiveness.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.730
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.012
GPT teacher head0.253
Teacher spread0.240 · 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