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Record W1998949305 · doi:10.12735/jbm.v1i1p40

Increasing the Productivity of Arab SMEs through Knowledge Management

2012· article· en· W1998949305 on OpenAlex
Dahi Emine

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Business & Management · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityBusinessKnowledge managementIndustrial organizationComputer scienceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Today knowledge is considered to be the primary resource of competitive advantage. This paper analyzed and accessed the status of KM in SMEs in Arab countries. It also examined the factors that influence the adoption of KM for SMEs in the region. In this paper; a comprehensive literature study, a Knowledge Management’s personalization approach were undertaken to set a KM Personalization Framework. Many organizations have profited from KM because they recognize the importance of the KM in business growth and development. However the result of this paper found SMEs, in Arab countries have not realized this importance and still are vulnerable. The study takes this factor into consideration and proposed an adoption approach specific to SMEs in region countries. The study contributes on KM and SMEs literature in both Arab and developing countries and is one of the few studies conducted in the specific region countries.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.029
GPT teacher head0.257
Teacher spread0.229 · 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