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Record W4377103902 · doi:10.4018/jgim.323439

The Role of Governments in Driving Industry 4.0 Adoption in Emerging Countries

2023· article· en· W4377103902 on OpenAlexafffund
Muhammad Mohiuddin, Mohammad Nurul Hassan Reza, Sreenivasan Jayashree, Md. Samim Al-Azad, Slimane Ed‐Dafali

Bibliographic record

VenueJournal of Global Information Management · 2023
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsLakehead UniversityUniversité Laval
FundersUniversité Laval
KeywordsSubsidyBusinessGovernment (linguistics)Industrial organizationEmerging marketsPublic policyProcess (computing)Production (economics)Organizational structureIndustrial policyEconomicsMarket economyFinanceEconomic growthInternational tradeManagement

Abstract

fetched live from OpenAlex

Industry 4.0 contributes to the virtualization of production system and enhances capabilities. However, the adoption process poses substantial challenges for SMEs in emerging markets due to institutional voids, resources, and public supports. This study explores the role of government in adopting Industry 4.0 by the SMEs and how organizational structure influences the process. It employed a quantitative approach and surveyed 225 managers. Industry 4.0 adoption is significantly influenced by government policy and subsidies. Government policy and subsidy transform organizational structure to be more transparent and flexible, streamlining them in adopting Industry 4.0. The organizational structure substantially mediates the relationships between government policy, subsidy, and Industry 4.0 adoption. This study implies that governments are vital in helping SMEs to adopt Industry 4.0 in emerging markets. Thus, governments should make policies that support technology adoption by offering sufficient funding/subsidies to boost innovation and technological transformation.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations35
Published2023
Admission routes2
Has abstractyes

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