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Record W4392293561 · doi:10.1080/09537287.2024.2321284

A meta-analysis of the relationship between quality management and innovation in small and medium-sized enterprises

2024· article· en· W4392293561 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

VenueProduction Planning & Control · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsQuality (philosophy)BusinessKnowledge managementTotal quality managementManagement stylesEmpirical researchQuality managementLeadership styleInnovation managementMarketingManagementLean manufacturingComputer scienceEconomics

Abstract

fetched live from OpenAlex

This paper presents a comprehensive meta-analysis examining the relationship between Quality Management (QM) and innovation in Small and Medium-Sized Enterprises (SMEs). Through a statistical synthesis of the findings of 31 empirical studies published between 2008 and 2022, this meta-analysis reveals a significant positive correlation between QM and diverse innovation types in SMEs. More specifically, the results show that total quality management, soft and hard quality management practices and quality management systems all positively correlate with technological, non-technological and green innovations. Importantly, the results underscore the pivotal role of leadership styles – charismatic, team-oriented, participative and autonomous – in enhancing the QM-innovation relationship, while human-oriented and self-protective styles appear to diminish it. The findings offer strategic insights for SMEs managers to optimize innovation through tailored quality initiatives and leadership style.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.172
GPT teacher head0.329
Teacher spread0.157 · 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