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Record W2746549395 · doi:10.5539/ass.v13n9p33

Quality Management Practice Influences Organizational Performance: A Case of a Vietnamese SME

2017· article· en· W2746549395 on OpenAlexvenueno aff
Giang Nguyen, Tran-Thuy-Duong Ninh

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsVietnameseBusinessScope (computer science)Customer satisfactionHuman resourcesOrganizational performanceHuman resource managementMarketingStrategic human resource planningQuality managementStrategic planningProcess managementKnowledge managementEnvironmental resource managementManagementEconomicsService (business)

Abstract

fetched live from OpenAlex

Quality Management Practice (QMP) has been proven its positive impact on organizational performance and received significant attention in recent years. However, there is little or no studies on QMP and organizational performance in Vietnam, specifically in Binh Duong. This research concentrated on finding the degree to which five elements of QMP namely Leadership, Strategic Planning, Process Management, Human Resource Management and Customer Satisfaction influence organizational performance within the scope of a SME in Binh Duong, Vietnam. In this study, a survey was conducted involving internal human resources and external customers, resulting in a response rate of 100 percent. Data was collected from three different perspectives of managers, staff and customer to gain the best insight understanding. The results of the survey revealed that Strategic Planning, Human Resources Management and Customer Satisfaction take important role to play in increasing performance of this organization while the other fundamentals needs more improvement to fully implement QMP at the company. This research has the potential for further research to enhance the standards of QMP in Binh Duong area.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.005
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 head0.029
GPT teacher head0.332
Teacher spread0.303 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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