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Record W2803950235 · doi:10.1142/s1363919619500166

STRATEGIC ORIENTATIONS, THE MEDIATING EFFECT OF ABSORPTIVE CAPACITY AND INNOVATION: A STUDY AMONG MALAYSIAN MANUFACTURING SMEs

2018· article· en· W2803950235 on OpenAlexaff
Abdullah Al Mamun, Syed Ali Fazal, Muhammad Mohiuddin, Zhan Su

Bibliographic record

VenueInternational Journal of Innovation Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité LavalThompson Rivers University
FundersMinistry of Higher Education, Malaysia
KeywordsAbsorptive capacityBusinessPerspective (graphical)Industrial organizationManufacturingBusiness administrationMarketingComputer science

Abstract

fetched live from OpenAlex

Strategic orientations (SOs) and absorptive capacity can significantly enhance innovation capacity in manufacturing Small and Medium Enterprises (SMEs). This study explores the relationship of SOs i.e., Market Orientations (MOs), Entrepreneurial Orientations (EOs) and Customer Orientations (COs) to absorptive capacity on the one hand, and to innovation on the other hand. The study also delves into the issue of how absorptive capacity mediates the effects of SOs on innovation in manufacturing SMEs from emerging countries’ perspective. This study uses a cross-sectional design and quantitative data collected through a structured interview of top managers from 360 manufacturing SMEs. The findings show that MO, EO and CO have positive and significant effects on innovation and that absorptive capacity partially mediates SOs’ effects on innovation.

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)0.003
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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