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Record W3143442745 · doi:10.1080/14783363.2021.1903308

The effects of HRM approach on quality management techniques and performance

2021· article· en· W3143442745 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTotal Quality Management & Business Excellence · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsHuman resource managementKnowledge managementControl (management)Management control systemBusinessQuality (philosophy)Process managementPsychologyOperations managementComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Using data from 250 companies from Brazil and Denmark, this study aims to investigate the effects of commitment- and control-oriented human resource management (HRM) on the relationship between four QM technique groups, namely goal setting (GS), continuous improvement (CI), measurement (MS) and failure prevention and control (FPC) techniques, and performance. Both HRM approaches affect the QM techniques and performance positively. However, the association with control-oriented HRM has a stronger performance effect for three QM techniques groups (CI, MS and FPC) than the association with commitment-oriented HRM. Only for the GS techniques, the effects of control- and commitment-oriented HRM on performance are not statistically significantly different. These results show that HRM practices may contribute to enable QM techniques to have a positive effect on performance. Additionally, the results demonstrate that control-oriented HRM supports the QM techniques better in improving performance than commitment-oriented HRM for most groups of QM techniques studied. These findings suggest an important duality: while previous studies suggest that QM practices thrive in a commitment-oriented HRM environment, this research shows that QM techniques are best supported through control-oriented HRM. Further research, going beyond the two country samples, is needed to explore the implications of this duality.

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)0.003
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 score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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