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Record W2066305976 · doi:10.1108/01443570510599692

Antecedents and performance outcomes of advanced manufacturing systems sophistication in SMEs

2005· article· en· W2066305976 on OpenAlexaffabout
Louis Raymond, Josée St‐Pierre

Bibliographic record

VenueInternational Journal of Operations & Production Management · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSophisticationBusinessContext (archaeology)Order (exchange)Industrial organizationKnowledge managementProcess managementSmall and medium-sized enterprisesMarketingOperations managementComputer scienceEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract Purpose – In order to deepen one's knowledge and further build theory on the implementation and use of advanced manufacturing systems (AMS) in small and medium‐sized enterprises (SMEs), the present research seeks to explore the following questions: What is the present level of AMS sophistication in SMEs? What characteristics of the SMEs' strategic, organizational and entrepreneurial context are associated with higher levels of AMS sophistication? And what are the operational and business performance impacts of this sophistication for small and medium‐sized manufacturers? Design/methodology/approach – A survey of 248 Canadian manufacturers was used to collect data that were analyzed by structured equation modeling. Findings – AMS sophistication significantly impacts both the operational performance and the business performance of SMEs. Antecedents of this sophistication include the education and experience of the owner‐manager, the strategic orientation of the firm, the type of production, and the commercial dependency of small manufacturers. Research limitations/implications – The nature of the sample and perceptual nature of certain measures impose care in generalizing the results of the study. Future research should examine environmental factors (e.g. environmental uncertainty) and structural factors (e.g. structural complexity) in particular for added explanatory power of AMS sophistication. Practical implications – Small business managers, wanting to increase their firm's manufacturing flexibility, reduce costs, improve quality, and eventually increase profitability, should look at the present level of AMS sophistication in conjunction with their strategic intent. Originality/value – Given the dearth of empirical knowledge in this regard, the present study has contributed to a better understanding of the nature and state of AMS sophistication in small manufacturing firms, and of the antecedents and outcomes of this sophistication.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations109
Published2005
Admission routes2
Has abstractyes

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