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Record W2474547740 · doi:10.19030/iber.v15i4.9715

An Empirical Assessment Of The EFQM Excellence Model In Purchasing

2016· article· en· W2474547740 on OpenAlexaff
David Hemsworth

Bibliographic record

VenueInternational Business & Economics Research Journal (IBER) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsNipissing University
Fundersnot available
KeywordsBusinessPurchasingQuality managementProcess managementEnablingStructural equation modelingCustomer satisfactionQuality (philosophy)BlueprintMarketingOperations managementComputer scienceEngineering

Abstract

fetched live from OpenAlex

This study focuses on the important concepts of quality management, internal customer satisfaction, and business performance within the neglected purchasing unit of manufacturing firms on the basis of the European Foundation for Quality Management (EFQM) Excellence Model, thus, filling a void in the existing literature. In doing so, this study tests the viability of the EFQM model in a single functional unit. Three hypothesis were generated based on the EFQM model to identify the specific relationships between purchasing’s quality management practices (EFQM enabler), internal customer satisfaction, and business performance (EFQM results). The hypotheses were tested through structural equation modeling based on a sample of 306 purchasing agents within manufacturing. The results indicated that the EFQM seem to be a viable model that represents what impacts implementing QMP enablers will have on the resultants, ICS and OP. Additionally, the results identified that the extent of adoption of quality management purchasing has a direct positive impact on improving internal customer satisfaction and an indirect positive impact on business performance mediated by internal customer satisfaction, as predicted by the EFQM model. This study highlights the positive impact of adoption of EFQM in the purchasing area, thus, lends support to purchasing departments trying to justify the implementation of quality management practices to their administrations. Additionally, it gives upper management, looking for ways to improve the company’s bottom line, the specifics to do this through the implementation of quality management practices in purchasing. Management and purchasing departments are given a blueprint for improving their performance.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
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 head0.115
GPT teacher head0.403
Teacher spread0.288 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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