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Record W2601015433 · doi:10.1108/jsbed-12-2015-0169

Quality management (QM) leads to healthier small businesses

2016· article· en· W2601015433 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

VenueJournal of Small Business and Enterprise Development · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsQuality (philosophy)Small businessMarketingBusinessPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to add to the current knowledge of how and why small businesses should engage in quality management (QM) by providing insights from small business owners who are committed advocates of QM. By so doing, to encourage small business owners to see that QM is right – and possible – for any small business wanting to improve performance. Design/methodology/approach Using an inductive method, semi-structured interviews followed a template of six open-ended questions. Study participants were ten owners of small family-owned business winners of a National Quality Award (National Housing Quality Award (NHQA)), making them industry leaders in applying QM. Data from these QM advocates are presented and discussed. Findings The cases reveal consistent encouragement for small businesses to engage in QM, with every owner certain that positive outcomes follow. Despite recognizing barriers to engagement, interviewees strongly feel the barriers are small relative to gains realized through QM. These QM advocates advise getting started by choosing one or a few QM tools and/or customizing tools rather than becoming overwhelmed by prospects of the complexity of doing QM to the exacting standards of various quality programs. Finally, they encourage small businesses to stay the course once started on QM. Research limitations/implications Limitations are that the paper relies on just ten case studies and these were taken from just one industry. While these limitations cannot be disputed, the rich data, interpretations, and opportunities for future research emerging from the inductive approach seem likely to resonate well beyond the particular industry involved here. Practical implications This paper speaks directly to small business owners by including many quotes from owners and summarizing themes from multiple interviews. The advice provided can be acted upon by any small business, with the opportunity of realizing improved business performance. Originality/value Few articles provide insights on the merits of QM for small businesses directly from interviews with small business owners. Here, the authors learn about the rationale for small businesses engaging in QM, are given thoughtful comments on how to get started, and told about the realities – including difficulties – of small business QM.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.256
Teacher spread0.218 · 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