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Record W4387353006 · doi:10.5267/j.ac.2023.8.003

Supply chain integration practices and its impact on financial and operational performance of the Tunisian industrial sector

2023· article· en· W4387353006 on OpenAlexvenueno aff
Rim Ghariani, Younes Boujelbène

Bibliographic record

VenueAccounting · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSupply chain managementSupply chainProcess managementQuality (philosophy)Customer relationship managementInformation sharingKnowledge managementMarketingOperations managementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Supply chain integration is a key factor in improving business performance. Despite this growing interest, little research has highlighted this issue that will determine the survival of many companies. The purpose of this study was to examine the relationship between the supply chain integration practices (SCMP) and the financial and operational performance of Tunisian manufacturing companies. To achieve this, a questionnaire was used as a research tool for data collection. Multiple regression analysis using SPSS26 software was used to answer study questions and examine study hypotheses. According to research, SCMP includes customer relationship management (CRM), supplier relationship management (SRM), buyer-supplier collaboration (BSC), joint knowledge creation (JCK), level of information sharing (IS), goal congruence (GC) , risk management (RM), internet usage (IU), quality management (QM) , except information quality (IQ), collaborative planning, forecasting and replenishment practices (CPFR), and just-in-time (JIT), are all significantly related to the financial and operational performance of the companies surveyed. Some recommendations are proposed to help managers better manage knowledge throughout the supply chain.

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)0.001
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 score0.102
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.047
GPT teacher head0.282
Teacher spread0.235 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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